Home
Search results “Text mining applications and theory of constraints”
Learning from Constraints
 
01:51:51
Rémi Coulom, jusqu'à très récemment auteur du meilleur programme du jeu de go au monde, parlera pendant une vingtaine de minutes sur CrazyStone, AlphaGo, et l'avenir de l'IA. Marco Gori, en visite de l'University of Siena, parlera sur "Learning from Constraints". Résumé : In this talk, I propose a functional framework to understand the emergence of intelligence in agents exposed to examples and knowledge granules. The theory is based on the abstract notion of constraint, which provides a representation of knowledge granules gained from the interaction with the environment. I give some representation theorems that extend the classic framework of kernel machines in such a way to incorporate logic formalisms, like first-order logic. This is made possible by the unification of continuous and discrete computational mechanisms in the same functional framework, so as any stimulus, like supervised examples and logic predicates, is translated into a constraint. The prescribed structure, which comes out from constrained variational calculus, is guided by a sort of parsimonious match of the constraints, and it is shown that only support constraints are involved, which nicely generalize the notion of support vectors in SVM. Finally, I present some experimental results that also include the verification of new constraints. Bio Marco Gori, University of Siena Marco Gori received the Ph.D. degree in 1990 from Università di Bologna, Italy, working partly at the School of Computer Science (McGill University, Montreal). In 1992, he became an Associate Professor of Computer Science at Università di Firenze and, in November 1995, he joint the Università di Siena, where he is currently full professor of computer science. His main interests are in machine learning with applications to pattern recognition, Web mining, and game playing. He is especially interested in bridging logic and learning Marco Gori received the Ph.D. degree in 1990 from Università di Bologna, Italy, and in the connections between symbolic and sub-symbolic representation of working partly at the School of Computer Science (McGill University, Montreal). In information. He was the leader of the WebCrow project for automatic solving of 1992, he became an Associate Professor of Computer Science at Università di Firenze crosswords, that outperformed human competitors in an official competition which and, in November 1995, he joint the Universitá di Siena, where he is currently full took place during the ECAI-06 conference. As a follow up of this grand challenge he founded QuestIt, a spin-off company of the University of Siena, working in the field of question-answering. He is co-author of the book "Web Dragons: Inside the myths of His main interests are in machine learning with applications to pattern recognition, search engines technologies," Morgan Kauffman (Elsevier), 2006. Web mining, and game playing. He is especially interested in bridging logic and learning and in the connections between symbolic and sub-symbolic representation of Dr. Gori serves (has served) as an Associate Editor of a number of technical journals information. He is the leader of the WebCrow project for automatic solving of crosswords, that outperformed human competitors in an official competition which related to his areas of expertise, he has been the recipient of best paper awards, and took place within the ECAI-06 conference. As a follow up of this grand challenge, he keynote speakers in a number of international conferences. He was the Chairman of the founded QuestIt, a spin-off company of the University of Siena, working in the field Italian Chapter of the IEEE Computational Intelligence Society, and the President of the of question-answering. He is co-author of the book “Web Dragons: Inside the myths Italian Association for Artificial Intelligence. of search engines technologies,” Morgan Kauffman (Elsevier), 2006. He is a fellow of the IEEE, ECCAI, IAPR. He is in the list of top Italian scientists kept by the VIA-Academy (http://www.topitalianscientists.org/top_italian_scientists.aspx) Lecture http://www.meetup.com/Nantes-Machine-Learning-Meetup/files/
Views: 1246 Aymeric Fouchault
Online Ranking with Constraints: A Primal-Dual Algorithm and Applications to Web Traffic-Shaping
 
03:59
Online Ranking with Constraints: A Primal-Dual Algorithm and Applications to Web Traffic-Shaping Parikshit Shah (Yahoo Research) Akshay Soni (Yahoo Research) Troy Chevalier (Yahoo Research) We study the online constrained ranking problem motivated by an application to web-traffic shaping: an online stream of sessions arrive in which, within each session, we are asked to rank items. The challenge involves optimizing the ranking in each session so that local vs. global objectives are controlled: within each session one wishes to maximize a reward (local) while satisfying certain constraints over the entire set of sessions (global). A typical application of this setup is that of page optimization in a web portal. We wish to rank items so that not only is user engagement maximized in each session, but also other business constraints (such as the number of views/clicks delivered to various publishing partners) are satisfied. We describe an online algorithm for performing this optimization. A novel element of our approach is the use of linear programming duality and connections to the celebrated Hungarian algorithm. This framework enables us to determine a set of \emph{shadow prices} for each traffic-shaping constraint that can then be used directly in the final ranking function to assign near-optimal rankings. The (dual) linear program can be solved off-line periodically to determine the prices. At serving time these prices are used as weights to compute weighted rank-scores for the items, and the simplicity of the approach facilitates scalability to web applications. We provide rigorous theoretical guarantees for the performance of our online algorithm and validate our approach using numerical experiments on real web-traffic data from a prominent internet portal. More on http://www.kdd.org/kdd2017/
Views: 294 KDD2017 video
IJDKP
 
00:41
International Journal of Data Mining & Knowledge Management Process (IJDKP) ISSN : 2230 - 9608 [Online] ; 2231 - 007X [Print] http://airccse.org/journal/ijdkp/ijdkp.html Call for papers :- Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the Journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Topics of interest include, but are not limited to, the following: Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] or [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 28 aircc journal
IJDKP
 
00:13
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum.Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects,surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining,Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining. Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks,Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing,OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper submission Authors are invited to submit papers for this journal through e-mail: [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 17 aircc journal
International Journal of Data Mining & Knowledge Management Process (IJDKP)
 
00:36
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 182 ijdkp jou
Multi-way analysis. Part 1. What is multi-way data
 
07:39
Quality and Technology group (www.models.life.ku.dk) Multi-way analysis series: A set of videos describing multi-way analysis (aka tensor modelling) and in particular PARAFAC modelling in chemometrics. The main videos give the theory and some have additional how-to videos showing how to approach modelling in MATLAB using PLS_Toolbox (www.eigenvector.com). You may also use the freely available N-way toolbox. This is available from www.models.life.ku.dk where you can also find data and e.g. MATLAB toolboxes for low-field NMR analysis, fluorescence analysis and many other things. Part 1. What is multi-way data (just a short intro to where we see multi-way data) Part 1b. What is multi-way data. MATLAB version (an intro to the EEM data and dataset object) Part 2. The PARAFAC model (the basic PARAFAC model) Part 2b. The PARAFAC model. MATLAB version (fitting PARAFAC in MATLAB) Part 3. What is good about PARAFAC (uniqueness, noise reduction, missing data) Part 3b. What is good about PARAFAC. MATLAB version (unique models in MATLAB) Part 4. The algorithm (about alternating least squares) Part 4b. The algorithm. MATLAB version (how to assess and handle convergence problems) Part 5. Number of components and outliers (core consistency and split-half) Part 5b. Number of components and outliers. MATLAB version (visualizing PARAFAC models) Part 6. Applications (fluorescence EEM applications) Part 7. More applications (low- and high-field NMR - DOSY) Part 8. Constraints (nonnegativity and beyond) Part 8b. Constraints. MATLAB version (and how to do it in MATLAB) Part 9. Concluding PARAFAC
Views: 7221 QualityAndTechnology
Associative Classification ll Classification Using Frequent Patterns Explained in Hindi
 
06:28
📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 8042 5 Minutes Engineering
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:10
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] **************************************************************************************** Call for Papers ============== Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations ======================= Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications ======================== Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing ==================== Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission **************** Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 5 aircc journal
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:07
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] **************************************************************************************** Call for Papers ============== Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations ======================= Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications ======================== Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing ==================== Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission **************** Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 14 aircc journal
IJDKP - May 2016
 
00:16
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdk... ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum.Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/ visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdk...
Views: 12 aircc journal
Data Mining Lecture -- Rule - Based Classification (Eng-Hindi)
 
03:29
-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 43253 Well Academy
International Journal of Data Mining & Knowledge Management Process  IJDKP
 
00:31
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/ visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:11
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum.Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/ visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 21 aircc journal
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:16
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] ******************************************************************* Call for Papers ============== Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations ======================= Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications ======================== Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing ==================== Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission **************** Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 52 aircc journal
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:10
http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] **************************************************************************************** Call for Papers ============== Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations ======================= Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications ======================== Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing ==================== Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission **************** Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. Important Dates **************** Submission Deadline : June 09, 2018 Notification : July 09, 2018 Final Manuscript Due : July 16, 2018 Publication Date : Determined by the Editor-in-Chief For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 4 aircc journal
Model fit during a Confirmatory Factor Analysis (CFA) in AMOS
 
10:07
This is a model fit exercise during a CFA in AMOS. I demonstrate how to build a good looking model, and then I address model fit issues, including modification indices and standardized residual covariances. I also discuss briefly the thresholds for goodness of fit measures. For a reference, you can use: Li􀀄tze Hu & Peter M. Bentler (1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives, Structural Equation Modeling: A Multidisciplinary Journal, 6:1, 1-55
Views: 424290 James Gaskin
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:10
Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 23 aircc journal
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:13
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum.Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/ visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 30 aircc journal
Linear Programming Problem (LPP) in R | Optimization | Operation Research
 
32:11
In this video you will be learning about Linear Programming Problems (LPP) and how to perform LPP in R. For study packs, consulting & training contact [email protected] ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 15063 Analytics University
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:10
Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 21 aircc journal
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:11
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] **************************************************************************************** Call for Papers ============== Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations ======================= Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications ======================== Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing ==================== Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission **************** Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. Important Dates **************** Submission Deadline : August 05, 2017 Notification : September 05, 2017 Final Manuscript Due : September 13, 2017 Publication Date : Determined by the Editor-in-Chief For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 35 aircc journal
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:11
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, ducational Data Mining Knowledge Processing Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 23 aircc journal
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:12
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/ Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdkp.html
Views: 17 Ijaia Journal
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
00:09
International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum.Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects,surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining,Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining. Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks,Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing,OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper submission Authors are invited to submit papers for this journal through e-mail: [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 49 aircc journal
Incorporating World Knowledge to Document Clustering via Heterogeneous Information Networks
 
18:58
Authors: Chenguang Wang, Yangqiu Song, Ahmed El-Kishky, Dan Roth, Ming Zhang, Jiawei Han Abstract: One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this paper, we provide an example of using world knowledge for domain dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, WordNet. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features. ACM DL: http://dl.acm.org/citation.cfm?id=2783374 DOI: http://dx.doi.org/10.1145/2783258.2783374
Tight Learning Bounds for Multi-Class Classification
 
38:04
Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document tagging in NLP, face recognition to learning universal feature representations in computer vision, gene function prediction in bioinformatics, etc. Extreme classification has also opened up a new paradigm for ranking and recommendation by reformulating them as multi-label learning tasks where each item to be ranked or recommended is treated as a separate label. Such reformulations have led to significant gains over traditional collaborative filtering and content-based recommendation techniques. Consequently, extreme classifiers have been deployed in many real-world applications in industry. This workshop aims to bring together researchers interested in these areas to encourage discussion and improve upon the state-of-the-art in extreme classification. In particular, we aim to bring together researchers from the natural language processing, computer vision and core machine learning communities to foster interaction and collaboration. Find more talks at - https://www.youtube.com/playlist?list=PLD7HFcN7LXReN-0-YQeIeZf0jMG176HTa
Views: 1264 Microsoft Research
Graph neural networks: Variations and applications
 
18:07
Many real-world tasks require understanding interactions between a set of entities. Examples include interacting atoms in chemical molecules, people in social networks and even syntactic interactions between tokens in program source code. Graph structured data types are a natural representation for such systems, and several architectures have been proposed for applying deep learning methods to these structured objects. I will give an overview of the research directions inside Microsoft that have explored different architectures and applications for deep learning on graph structured data. See more at https://www.microsoft.com/en-us/research/video/graph-neural-networks-variations-applications/
Views: 25744 Microsoft Research
International Journal of Data Mining & Knowledge Management Process (IJDKP)
 
00:11
International Journal of Data Mining & Knowledge Management Process (IJDKP) ISSN : 2230 - 9608 [Online] ; 2231 - 007X [Print] http://airccse.org/journal/ijdkp/ijdkp.html Call for papers :- Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the Journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Topics of interest include, but are not limited to, the following: Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] or [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 94 Sivakumar Arumugam
Scalable k-Means Clustering via Lightweight Coresets
 
02:53
Authors: Olivier Bachem (ETH Zurich); Mario Lucic (Google); Andreas Krause (ETH Zurich) Abstract: Coresets are compact representations of data sets such that models trained on a coreset are provably competitive with models trained on the full data set. As such, they have been successfully used to scale up clustering models to massive data sets. While existing approaches generally only allow for multiplicative approximation errors, we propose a novel notion of lightweight coresets that allows for both multiplicative and additive errors. We provide a single algorithm to construct lightweight coresets for k-means clustering as well as soft and hard Bregman clustering. The algorithm is substantially faster than existing constructions, embarrassingly parallel, and the resulting coresets are smaller. We further show that the proposed approach naturally generalizes to statistical k-means clustering and that, compared to existing results, it can be used to compute smaller summaries for empirical risk minimization. In extensive experiments, we demonstrate that the proposed algorithmoutperforms existing data summarization strategies in practice. More on http://www.kdd.org/kdd2018/
Views: 563 KDD2018 video
International Journal of Data Mining & Knowledge Management Process
 
00:11
International Journal of Data Mining & Knowledge Management Process (IJDKP) ISSN : 2230 - 9608 [Online] ; 2231 - 007X [Print] http://airccse.org/journal/ijdkp/ijdkp.html Call for papers :- Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the Journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Topics of interest include, but are not limited to, the following: Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] or [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 154 aircc journal
Building an OTT Platform in Under 24 Months with fuboTV (Cloud Next '19)
 
50:45
Creating a high-reliability, low-latency optimal video-streaming experience for an audiences is difficult. And creating that same experience for live sports is a monumental challenge. Learn how independent live TV streaming service fuboTV built and stabilized an OTT streaming platform in under 18 months, achieving +100% YoY subscriber growth, as well as international expansion. FuboTV CTO, Geir Magnusson, and Google Cloud's Neeve Nikoo will share best practices/challenges on microservices, cloud media architectures, and performant cloud video systems. Building an OTT Platform → http://bit.ly/2K4Ah9W Watch more: Next '19 Architecture Sessions here → https://bit.ly/Next19Architecture Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions Subscribe to the GCP Channel → https://bit.ly/GCloudPlatform Speaker(s): Geir Magnusson, Neeve Nikoo Session ID: ARC105 product:Cloud Storage,Kubernetes Engine,Virtual Private Cloud (VPC); fullname:Neeve Nikoo;
Transforming Healthcare With Machine Learning (Cloud Next '19)
 
42:38
With the wealth of medical imaging and text data available, there’s a big opportunity for machine learning to optimize healthcare workflows. In this talk, we’ll provide an overview of the Cloud ML products that can help with healthcare scenarios, including AutoML Vision, Cloud Natural Language, and BigQuery ML. Then we’ll hear from IDEXX, a veterinary diagnostics company using AutoML Vision to classify radiology images. Beyond Just Speech-To-Text → https://bit.ly/2TS6twZ Watch more: Next '19 ML & AI Sessions here → https://bit.ly/Next19MLandAI Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions Subscribe to the GCP Channel → https://bit.ly/GCloudPlatform Speaker(s): Francisco Uribe, Ben Litchfield Session ID: MLAI218 product:AutoML Vision; fullname:Francisco Uribe;
Lecture 7 | Machine Learning (Stanford)
 
01:15:45
Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/zJX/ Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on optimal margin classifiers, KKT conditions, and SUM duals. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford
Views: 155393 Stanford
How DTW (Dynamic Time Warping) algorithm works
 
07:00
In this video we describe the DTW algorithm, which is used to measure the distance between two time series. It was originally proposed in 1978 by Sakoe and Chiba for speech recognition, and it has been used up to today for time series analysis. DTW is one of the most used measure of the similarity between two time series, and computes the optimal global alignment between two time series, exploiting temporal distortions between them. Source code of graphs available at https://github.com/tkorting/youtube/blob/master/how-dtw-works.m The presentation was created using as references the following scientific papers: 1. Sakoe, H., Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustic Speech and Signal Processing, v26, pp. 43-49. 2. Souza, C.F.S., Pantoja, C.E.P, Souza, F.C.M. Verificação de assinaturas offline utilizando Dynamic Time Warping. Proceedings of IX Brazilian Congress on Neural Networks, v1, pp. 25-28. 2009. 3. Mueen, A., Keogh. E. Extracting Optimal Performance from Dynamic Time Warping. available at: http://www.cs.unm.edu/~mueen/DTW.pdf
Views: 42670 Thales Sehn Körting
Linear Programming
 
11:11
Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !! **DOH! There is a STUPID arithmetic mistake by me at the very end!** Sorry! Linear Programming. I do a complete example! For more free math videos, visit http://PatrickJMT.com
Views: 992702 patrickJMT
An Introduction to Temporal Databases
 
50:10
In the past manipulating temporal data was rather ad hoc and in the form of simple solutions. Today organizations strongly feel the need to support temporal data in a coherent way. Consequently, there is an increasing interest in temporal data and major database vendors recently provide tools for storing and manipulating temporal data. However, these tools are far from being complete in addressing the main issues in handling temporal data. The presentation uses the relational data model in addressing the subtle issues in managing temporal data: comparing database states at two different time points, capturing the periods for concurrent events and accessing to times beyond these periods, sequential semantics, handling multi-valued attributes, temporal grouping and coalescing, temporal integrity constraints, rolling the database to a past state and restructuring temporal data, etc. It also lays the foundation in managing temporal data in NoSQL databases as well. Having ranges as a data type PostgresSQL has a solid base in implementing a temporal database that can address many of these issues successfully. About the Speaker Abdullah Uz Tansel is professor of Computer Information Systems at the Zicklin School of Business at Baruch College and Computer Science PhD program at the Graduate Center. His research interests are database management systems, temporal databases, data mining, and semantic web. Dr. Tansel published many articles in the conferences and journals of ACM and IEEE. Dr. Tansel has a pending patent application on semantic web. Currently, he is researching temporality in RDF and OWL, which are semantic web languages. Dr. Tansel served in program committees of many conferences and headed the editorial board that published the first book on temporal databases in 1993. He is also one the editors of the forth coming book titled Recommendation and Search in Social Networks to be published by Springer. He received BS, MS and PhD degrees from the Middle East Technical University, Ankara Turkey. He also completed his MBA degree in the University of Southern California. Dr. Tansel is a member of ACM and IEEE Computer Society.
Views: 618 Postgres Conference
Part 1.1| DATA INFORMATION DATA BASE DATA BASE MANAGEMENT SYSTEM definition difference what
 
16:05
• Counselling Guruji is our latest product & a well-structured program that answers all your queries related to Career/GATE/NET/PSU’s/Private Sector etc. You can register for the program at: https://goo.gl/forms/ZmLB2XwoCIKppDh92 You can check out the brochure at: https://www.google.com/url?q=http://www.knowledgegate.in/guruji/counselling_guruji_brochure.pdf&sa=D&ust=1553069285684000&usg=AFQjCNFaTk4Pnid0XYyZoDTlAtDPUGcxNA • Link for the complete playlist of DBMS is: https://www.youtube.com/playlist?list=PLmXKhU9FNesR1rSES7oLdJaNFgmuj0SYV • Links for the books that we recommend for DBMS are: 1.Database System Concepts (Writer: Avi Silberschatz · Henry F.Korth · S. Sudarshan) (Publisher: McGraw Hill Education) https://amzn.to/2HoR6ta 2.Fundamentals of database systems (Writer:Ramez Elmsari,Shamkant B.Navathe) https://amzn.to/2EYEUh2 3.Database Management Systems (Writer: Raghu Ramkrishnan, JohannesGehrke) https://amzn.to/2EZGYph 4.Introduction to Database Management (Writer: Mark L. Gillenson, Paulraj Ponniah, Alex Kriegel, Boris M. Trukhnov, Allen G. Taylor, and Gavin Powell with Frank Miller.(Publisher: Wiley Pathways) https://amzn.to/2F0e20w • Check out our website http://www.knowledgegate.in/ • Please spare some time and fill this form so that we can know about you and what you think about us: https://goo.gl/forms/b5ffxRyEAsaoUatx2 • Your review/recommendation and some words can help validating our quality of content and work so Please do the following: - 1) Give us a 5-star review with comment on Google https://goo.gl/maps/sLgzMX5oUZ82 2) Follow our Facebook page and give us a 5-star review with comments https://www.facebook.com/pg/knowledgegate.in/reviews 3) Follow us on Instagram https://www.instagram.com/mail.knowledgegate/ 4) Follow us on Quora https://www.quora.com/profile/Sanchit-Jain-307 • Links for Hindi playlists of other Subjects are: TOC: https://www.youtube.com/playlist?list=PLmXKhU9FNesSdCsn6YQqu9DmXRMsYdZ2T OS: https://www.youtube.com/playlist?list=PLmXKhU9FNesSFvj6gASuWmQd23Ul5omtD Digital Electronics: https://www.youtube.com/playlist?list=PLmXKhU9FNesSfX1PVt4VGm-wbIKfemUWK Discrete Mathematics: Relations:https://www.youtube.com/playlist?list=PLmXKhU9FNesTpQNP_OpXN7WaPwGx7NWsq Graph Theory: https://www.youtube.com/playlist?list=PLmXKhU9FNesS7GpOddHDX3ZCl86_cwcIn Group Theory: https://www.youtube.com/playlist?list=PLmXKhU9FNesQrSgLxm6zx3XxH_M_8n3LA Proposition:https://www.youtube.com/playlist?list=PLmXKhU9FNesQxcibunbD82NTQMBKVUO1S Set Theory: https://www.youtube.com/playlist?list=PLmXKhU9FNesTSqP8hWDncxpCj8a4uzmu7 Data Structure: https://www.youtube.com/playlist?list=PLmXKhU9FNesRRy20Hjr2GuQ7Y6wevfsc5 Computer Networks: https://www.youtube.com/playlist?list=PLmXKhU9FNesSjFbXSZGF8JF_4LVwwofCd Algorithm: https://www.youtube.com/playlist?list=PLmXKhU9FNesQJ3rpOAFE6RTm-2u2diwKn • About this video: This video explains the meaning of database, database management system, data and information. What is the difference between data and information, what is the difference between database and dbms, meaning of database, purpose of dbms, advantages of database management system, components of database are discussed. Data:Raw and isolated facts about an entity. Data can be of any kind like audio data, text data, graphic etc Information: Data when processed and become meaningful for the user is called information. Database: Collection of related/similar and relevant data is called as database. DBMS: • A DBMS is a collection of programs that enable users to create and maintain a database. • The primary goal of a database system is to provide a way to store and retrieve database information that is both convenient and efficient database tutorial in hindi, definition of data in dbms, components of dbms in hindi,difference between database and dbms, dbms tutorials for gate, dbms for beginners in hindi, 3-tier architecture of dbms in hindi,dbms for net,knowledge gate dbms, DBMS blueprint, DataBase Management system,database,DBMS, RDBMS, Relations, Table, Query, Normalization, Normal forms,Database design,Relational Model,Instance,Schema,Data Definition Language, SQL queries, ER Diagrams, Entity Relationship Model,Constraints,Entity,Attributes,Weak entity, Types of entity,DataBase design, database architecture, Degree of relation,Cardinality ratio,One to many relationship,Many to many relationships,Relational Algebra,Relational Calculus, Tuples, Natural Join, Join operations,Database Architecture,database Schema, Keys in DBMS, Primary keys, Candidate keys, Foreign keys,Data redundancy, Duplicacy in data, Data Inconsistency, Normalization, First Normal Form,Second Normal Form, third normal forms, Boye codd's normal form,1NF,2NF,3NF,BCNF, Normalization rules, Decomposition of relation, Functional Dependency,Partial Dependency, Multivalued dependency,Indexing,Hashing, B tree,B+ tree,Ordered Indexing,Select operation,Join operations,
Views: 82285 KNOWLEDGE GATE
NIPS 2015 Workshop (Walter) 15481 Multimodal Machine Learning
 
20:31
lt b gt Workshop Overview lt /b gt lt br gt Multimodal machine learning aims at building models that can process and relate information from multiple modalities. From the early research on audio-visual speech recognition to the recent explosion of interest in models mapping images to natural language, multimodal machine learning is is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential. lt br gt Learning from paired multimodal sources offers the possibility of capturing correspondences between modalities and gain in-depth understanding of natural phenomena. Thus, multimodal data provides a means of reducing our dependence on the more standard supervised learning paradigm that is inherently limited by the availability of labeled examples. lt br gt lt br gt This research field brings some unique challenges for machine learning researchers given the heterogeneity of the data and the complementarity often found between modalities. This workshop will facilitate the progress in multimodal machine learning by bringing together researchers from natural language processing, multimedia, computer vision, speech processing and machine learning to discuss the current challenges and identify the research infrastructure needed to enable a stronger multidisciplinary collaboration. lt br gt lt br gt For keynote talk abstracts and MMML 2015 workshop proceedings: lt br gt lt a href="https://sites.google.com/site/multiml2015/" gt https://sites.google.com/site/multiml2015/ lt /a gt lt br gt lt br gt lt b gt Oral presentation lt /b gt lt br gt - Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences - lt i gt Hongyuan Mei, Mohit Bansal, Matthew Walter lt /i gt lt br gt lt br gt lt b gt Oral spotlights lt /b gt lt br gt - An Analysis-By-Synthesis Approach to Multisensory Object Shape Perception. lt i gt Goker Erdogan, Ilker Yildirim, Robert Jacobs lt /i gt lt br gt - Active Perception based on Multimodal Hierarchical Dirichlet Processes. lt i gt Tadahiro Taniguchi, Toshiaki Takano, Ryo Yoshino lt /i gt lt br gt - Towards Deep Alignment of Multimodal Data. lt i gt George Trigeorgis, Mihalis Nicolaou, Stefanos Zafeiriou, Bjorn Schuller lt /i gt lt br gt - Multimodal Transfer Deep Learning with an Application in Audio-Visual Recognition. lt i gt Seungwhan Moon, Suyoun Kim, Haohan Wang lt /i gt lt br gt lt br gt lt b gt Posters lt /b gt lt br gt - Multimodal Convolutional Neural Networks for Matching Image and Sentence. lt i gt Lin Ma, Zhengdong Lu, Lifeng Shang, Hang Li lt /i gt lt br gt - Group sparse factorization of multiple data views. lt i gt Eemeli Leppaaho, Samuel Kaski lt /i gt lt br gt - Unveiling the Dreams of Word Embeddings: Towards Language-Driven Image Generation. lt i gt Angeliki Lazaridou, Dat Tien Nguyen, Raffaella Bernardi, Marco Baroni lt /i gt lt br gt - Cross-Modal Attribute Recognition in Fashion. lt i gt Susana Zoghbi, Geert Heyman, Juan Carlos Gomez Carranza, Marie-Francine Moens lt /i gt lt br gt - Multimodal Sparse Coding for Event Detection. lt i gt Youngjune Gwon, William Campbell, Kevin Brady, Douglas Sturim, Miriam Cha, H. T. Kung lt /i gt lt br gt - Multimodal Symbolic Association using Parallel Multilayer Perceptron. lt i gt Federico Raue, Sebastian Palacio, Thomas Breuel, Wonmin Byeon, Andreas Dengel, Marcus Liwicki lt /i gt lt br gt - Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning. lt i gt Janarthanan Rajendran, Mitesh Khapra, Sarath Chandar, Balaraman Ravindran lt /i gt lt br gt - Multimodal Learning of Object Concepts and Word Meanings by Robots. lt i gt Tatsuya Aoki, Takayuki Nagai, Joe Nishihara, Tomoaki Nakamura, Muhammad Attamimi lt /i gt lt br gt - Multi-task, Multi-Kernel Learning for Estimating Individual Wellbeing. lt i gt Natasha Jaques, Sara Taylor, Akane Sano, Rosalind Picard lt /i gt lt br gt - Generating Images from Captions with Attention. lt i gt Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov lt /i gt lt br gt - Manifold Alignment Determination. lt i gt Andreas Damianou, Neil Lawrence, Carl Henrik Ek lt /i gt lt br gt - Accelerating Multimodal Sequence Retrieval with Convolutional Networks. lt i gt Colin Raffel, Daniel P. W. Ellis lt /i gt lt br gt - Audio-Visual Fusion for Noise Robust Speech Recognition. lt i gt Nagasrikanth Kallakuri, Ian Lane lt /i gt lt br gt - Learning Multimodal Semantic Models for Image Question Answering. lt i gt Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng lt /i gt lt br gt - Greedy Vector-valued Multi-view Learning. lt i gt Hachem Kadri, Stephane Ayache, Cecile Capponi, Francois-Xavier Dupe lt /i gt lt br gt - S2VT: Sequence to Sequence -- Video to Text. lt i gt Subhashini Venugopalan, Marcus Rohrbach, Jeff Donahue, Raymond Mooney, Trevor Darrell, Kate Saenko lt /i gt
Views: 177 NIPS
Panel Discussion: Ai-Perception and Applications
 
01:16:45
The Academic Research Summit, co-organized by Microsoft Research and the Association for Computing Machinery, is a forum to foster meaningful discussion among the Indian computer science research community and raise the bar on research efforts. The third edition of Academic Research Summit was held at the International Institute of Information Technology (IIIT) Hyderabad on the 24th and 25th of January 2018. The agenda included keynotes and talks from distinguished researchers from India and across the world. The summit also had sessions focused on specific topics related to the theme of Artificial Intelligence: A Future with AI. More talks at: https://www.youtube.com/playlist?list=PLD7HFcN7LXRcBpyp34moH_-dVJTX7bXxW
Views: 1400 Microsoft Research
Mathematical optimization model that helps with decision-making in uncertain situations
 
03:53
Akiko Takeda's research group works on mathematical optimization and related issues. A mathematical optimization model is used to find the "best available" value of some objective function under given constraints. It helps with making rational decisions, such as planning factory production, or finding the shortest route using given modes of transport. In conventional mathematical optimization models, it's been necessary to anticipate and model one future condition, such as product demand. But nowadays, the social environment is changing so rapidly, it's difficult to anticipate even one future condition. In that case, what's needed is a method for making decisions by considering all situations that might occur. So the Takeda Group is researching a method called robust optimization. This decision-making method is "robust" because it can handle uncertain changes in conditions. Q. "Robust optimization originated around 1998, so it's still in the process of development. This method is based on the need to deal with uncertain things, and it continually anticipates the worst-case scenario, so that even if the worst does happen, people can see how far a good solution is available. When a business makes a production plan, the model is based entirely on future expectations: what the future demand will be, how much materials will cost, and so on. So even if the expectations are incorrect, this modeling method is "robust" with regard to them." Currently, one of the Group's research topics using robust optimization is panel-size optimization for solar photovoltaic systems. The method uses mathematical expressions to determine the optimal size of panels to satisfy land and cost constraints at the system's location and to meet numerical targets for CO2 reduction. In this work, one crucial point is how much to consider uncertainties, such as the amount of sunlight. Q. "Because photovoltaic electricity depends so much on the availability of sunlight, its output declines if there's a succession of rainy days. In that sense, there's uncertainty regarding the amount of sunlight available. So, using daily data for the 10 years from 2000 to 2009, we calculate the range in which the sunlight varies, and make a forecast based on 10 years' worth of data. We are then able to decide, through a statistical method, the range of the amount of sunlight with 0.95 probability. The Takeda Group is applying its predictive models, which consider uncertainty using robust optimization, to the problem of discrimination in machine learning. Machine learning is used in a diverse range of fields that require discrimination, including medical diagnostics, spam filtering, financial market prediction, and text recognition. The Group aims to develop a model that enables machines to discriminate with high precision, even if the data includes noise. Q. "Right now, we're at the very first stage, having used robust optimization to make decisions for solar photovoltaic systems. If we can receive requests and feedback from interested people, we'd like to include those in the model, to make it more complex. That's what we'd like to do from now on."
Lecture 15: Coreference Resolution
 
01:20:46
Lecture 15 covers what is coreference via a working example. Also includes research highlight "Summarizing Source Code", an introduction to coreference resolution and neural coreference resolution. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
The Power of Theory in the Practice of Hashing with Focus on Similarity Estimation
 
01:07:17
A Google TechTalk, 3/8/18, presented by Mikkel Thorup (University of Copenhagen) Talks from visiting speakers on Algorithms, Theory, and Optimization
Views: 1253 GoogleTechTalks
Linear Programming decoder In NLP Part 2
 
01:28:34
Linear Programming Decoders in Natural Language Processing: From Integer Programming to Message Passing and Dual Decomposition André F. T. Martins October 25, 2014 - Afternoon Tutorial notes Abstract: This tutorial will cover the theory and practice of linear programming decoders. This class of decoders encompasses a variety of techniques that have enjoyed great success in devising structured models for natural language processing (NLP). Along the tutorial, we provide a unified view of different algorithms and modeling techniques, including belief propagation, dual decomposition, integer linear programming, Markov logic, and constrained conditional models. Various applications in NLP will serve as a motivation. There is a long string of work using integer linear programming (ILP) formulations in NLP, for example in semantic role labeling, machine translation, summarization, dependency parsing, coreference resolution, and opinion mining, to name just a few. At the heart of these approaches is the ability to encode logic and budget constraints (common in NLP and information retrieval) as linear inequalities. Thanks to general purpose solvers (such as Gurobi, CPLEX, or GLPK), the practitioner can abstract away from the decoding algorithm and focus on developing a powerful model. A disadvantage, however, is that general solvers do not scale well to large problem instances, since they fail to exploit the structure of the problem. This is where graphical models come into play. In this tutorial, we show that most logic and budget constraints that arise in NLP can be cast in this framework. This opens the door for the use of message-passing algorithms, such as belief propagation and variants thereof. An alternative are algorithms based on dual decomposition, such as the subgradient method or AD3. These algorithms have achieved great success in a variety of applications, such as parsing, corpus-wide tagging, machine translation, summarization, joint coreference resolution and quotation attribution, and semantic role labeling. Interestingly, most decoders used in these works can be regarded as structure-aware solvers for addressing relaxations of integer linear programs. All these algorithms have a similar consensus-based architecture: they repeatedly perform certain "local" operations in the graph, until some form of local agreement is achieved. The local operations are performed at each factor, and they range between computing marginals, max-marginals, an optimal configuration, or a small quadratic problem, all of which are commonly tractable and efficient in a wide range of problems. As a companion of this tutorial, we provide an open-source implementation of some of the algorithms described above, available at http://www.ark.cs.cmu.edu/AD3. Instructors: André F. T. Martins, research scientist, Instituto de Telecomunicações, Instituto Superior Técnico, and Priberam Informática A. Martins is a research scientist at Priberam Labs. He received his dual-degree PhD in Language Technologies in 2012 from Carnegie Mellon University and Instituto Superior Técnico. His PhD dissertation was awarded Honorable Mention in CMU’s SCS Dissertation Award competition. Martins' research interests include natural language processing, machine learning, structured prediction, sparse modeling, and optimization. His paper "Concise Integer Linear Programming Formulations for Dependency Parsing" received a best paper award at ACL 2009.
Views: 358 emnlp acl
A Parallel and Primal-Dual Sparse Method for Extreme Classification
 
01:53
A Parallel and Primal-Dual Sparse Method for Extreme Classification Ian Yen (Carnegie Mellon University) Xiangru Huang (University of Texas at Austin) Wei Dai (Carnegie Mellon University) Pradeep Ravikumar (Carnegie Mellon University) Inderjit Dhillon (University of Texas at Austin) Eric Xing (Carnegie Mellon University) Extreme Classification considers the problem of multiclass or multilabel prediction when there is a huge number of classes: a scenario that occurs in many real-world applications such as text and image tagging. In this setting, standard classification methods with complexity linear to the number of classes become intractable, while enforcing structural constraints among classes (such as low-rank or tree-structured) to reduce complexity often sacrifices accuracy for efficiency. The recent \emph{PD-Sparse} method addresses this issue to gives an algorithm that is sublinear in the number of variables by exploiting \emph{primal-dual} sparsity inherent in the max-margin loss. However, the objective requires training models of all classes together, which incurs large memory consumption and prohibits it from the simple parallelization scheme that a one-versus-all method can easily take advantage of. In this work, we propose a primal-dual sparse method that enjoys the same parallelizability and space efficiency of one-versus-all approach, while having complexity sublinear to the number of classes. On several large-scale benchmark data sets, the proposed method achieves accuracy competitive to state-of-the-art methods while reducing training time from days to tens of minutes compared to existing parallel or sparse methods on a cluster of $100$ cores. More on http://www.kdd.org/kdd2017/
Views: 195 KDD2017 video
Trinity On using Trinary Trees for Unsupervised Web Data Extraction
 
03:50
We are ready to provide guidance to successfully complete your projects. IEEE 2014 Projects : http://www.squaresoft.co.in/
Re-Computing Social Sciences: Flash Talks, Session 2
 
01:04:37
Featuring 5-minute presentations by Cuihua Shen, Joshua Blumentstock, Jana Diesner, Chris Smith, Sandra Gonzalez-Bailon, Duncan Temple Lang, and Xiaoling Shu.
HLS Library Book Talk: "Big Data, Health Law, and Bioethics"
 
01:20:50
On Wednesday Sept. 12, the Harvard Law School Library hosted a book talk and discussion in celebration of the recent publication of "Big Data, Health Law, and Bioethics," edited by I. Glenn Cohen, Holly Fernandez Lynch, Urs Gasser, and Effy Vayena. The talk was co-sponsored by the Petrie-Flom Center for Health Law Policy, Biotechnology and Bioethics and by the Berkman Klein Center for Internet & Society at Harvard University.
Views: 1022 Harvard Law School
ALIEN 2.0: The Infinite Memory
 
07:06:12
Abstract— Visual data is massive, is growing faster than our ability to store or index it [1] [2] and the cost of manual annotation is critically expensive. Effective methods for unsupervised learning are of paramount need. A possible scenario is that of considering visual data coming in the form of streams. In dynamically changing and non-stationary environments, the data distribution can change over time yielding the general phenomenon of concept drift [3], [4], [5] which violates the basic assumption of traditional machine learning algorithms (iid). This demo presents our recent results in learning an instancelevel object detector from a potentially infinitely long video-stream (i.e. YouTube). This is an extremely challenging problem largely unexplored, since a great deal of work has been done on learning under the iid assumption [6], [7], [8]. Our approach starts from the recent success of long term object tracking [9], [10], [11], [12], [13], [14] extending our previously developed [12] and demostrated [15], [16], [17] method (ALIEN). The novel contribution is the introduction of an online appearance learning procedure based on a incremental condensing [18] strategy which is shown to be asymptotically stable. Asymptotic stability evidence will be interactively evaluated by attendants based on a real time face tracking application using webcam or YouTube data. References [1] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 248–255. IEEE, 2009. [2] P. Perona. Vision of a visipedia. Proceedings of the IEEE, 98(8):1526 –1534, aug. 2010. [3] Jeffrey C. Schlimmer and Richard H. Granger, Jr. Incremental learning from noisy data. Mach. Learn., 1(3):317–354, March 1986. [4] Gerhard Widmer and Miroslav Kubat. Learning in the presence of concept drift and hidden contexts. Machine learning, 23(1):69–101, 1996. [5] Jo˜ao Gama, Indr˙e ˇZliobait˙e, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. A survey on concept drift adaptation. ACM Comput. Surv., 46(4):44:1–44:37, March 2014. [6] Vladimir N Vapnik and A Ya Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability & Its Applications, 16(2):264–280, 1971. [7] Bernhard E Boser, Isabelle M Guyon, and Vladimir N Vapnik. A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory, pages 144–152. ACM, 1992. [8] Yoav Freund, Robert E Schapire, et al. Experiments with a new boosting algorithm. 1996. [9] Z. Kalal, J. Matas, and K. Mikolajczyk. P-n learning: Bootstrapping binary classifiers by structural constraints. In CVPR, june 2010. [10] Karel Lebeda, Simon Hadfield, Jiri Matas, and Richard Bowden. Long- term tracking through failure cases. In Proceeedings, IEEE workshop on visual object tracking challenge at ICCV 2013, Sydney, Australia, 2 December 2013. IEEE, IEEE. [11] Supancic and D. Ramanan. Self-paced learning for long-term tracking. Computer Vision and Pattern Recognition (CVPR), 2013. [12] Federico Pernici and Alberto Del Bimbo. Object tracking by oversam- pling local features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 99(PrePrints):1, 2013. [13] Yang Hua, Karteek Alahari, and Cordelia Schmid. Occlusion and motion reasoning for long-term tracking. In Computer Vision–ECCV 2014, pages 172–187. Springer, 2014. [14] Zhibin Hong, Zhe Chen, Chaohui Wang, Xue Mei, Danil Prokhorov, and Dacheng Tao. Multi-store tracker (muster): A cognitive psychology inspired approach to object tracking. June 2015. [15] Federico Pernici. Facehugger: The alien tracker applied to faces. In Computer Vision–ECCV 2012. Workshops and Demonstrations, pages 597–601. Springer, 2012. [16] Federico Pernici. Facehugger: The alien tracker applied to faces. In CVPR 2012. Workshops and Demonstrations, 2012. [17] Federico Pernici. Back to back comparison of long term tracking systems. In ICCV 2013. Workshops and Demonstrations, 2013. [18] P. E. Hart. The condensed nearest neighbor rule. IEEE Transactions on Information Theory, 1968.
Views: 266 Federico Pernici