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Search results “Text mining applications and theory of constraints”
Data Reduction Techniques:Theory & Practice I
 
31:39
This session will give the introductory information on reduction techniques and introduce one of the well known applications of such known as data deduplication. Data deduplication and other methods of reducing storage consumption play a vital role in affordably managing today’s explosive growth of data. Optimizing the use of storage is part of a broader strategy to provide an efficient information infrastructure that is responsive to dynamic business requirements. This presentation will explore the significance of deduplication from both the theoretical and practiocal aspects related to specific capacity optimization techniques within the context of information lifecycle management. The benefits of optimizing storage capacity span cost savings, risk reduction, and process improvement. Capital expenditures on networked storage equipment and floor space can be reduced or deferred. Ongoing operating expenses for power, cooling, and labor can also be reduced because there is less equipment to operate and manage. Increasing the efficiency and effectiveness of their storage environments helps companies remove constraints on data growth, improve their service levels, and better leverage the increasing quantity and variety of data to improve their competitiveness.
Views: 2563 Şuayb Ş. Arslan
What is CONTEXTUAL SEARCHING? What does CONTEXTUAL SEARCHING mean? CONTEXTUAL SEARCHING meaning
 
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What is CONTEXTUAL SEARCHING? What does CONTEXTUAL SEARCHING mean? CONTEXTUAL SEARCHING meaning - CONTEXTUAL SEARCHING definition - CONTEXTUAL SEARCHING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Contextual search is a form of optimizing web-based search results based on context provided by the user and the computer being used to enter the query. Contextual search services differ from current search engines based on traditional information retrieval that return lists of documents based on their relevance to the query. Rather, contextual search attempts to increase the precision of results based on how valuable they are to individual users. The basic form of contextual search is the process of scanning the full-text of a query in order to understand what the user needs. Web search engines scan HTML pages for content and return an index rating based on how relevant the content is to the entered query. HTML pages that have a higher occurrence of query keywords within their content are not rated higher. Users have limited control over the context of their query based on the words they use to search with. For example, users looking for the menu portion of a website can add “menu” to the end of their query to provide the search engine with context of what they need. The next step in contextualizing search is for the search service itself to request information that narrows down the results, such as Google asking for a time range to search within. Certain search services, including many Meta search engines, request individual contextual information from users to increase the precision of returned documents. Inquirus 2 is a Meta search engine that acts as a mediator between the user query and other search engines. When searching on Inquirus 2, users enter a query and specify constraints such as the information need category, maximum number of hits, and display formats. For example, a user looking for research papers can specify documents with “references” or “abstracts” to be rated higher. If another user is searching for general information on the topic rather than research papers, they can specify the GenScore attribute to have a heavier weight. Explicitly supplied context effectively increases the precision of results, however, these search services tend to suffer from poor user-experience. Learning the interface of programs like Inquirus can prove challenging for general users without knowledge of search metrics. Aspects of supplied context do appear on major search engines with better user-interaction such as Google and Bing. Google allows users to filter by type: Images, Maps, Shopping, News, Videos, Books, Flights, and Apps. Google has an extensive list of search operators that allow users to explicitly limit results to fit their needs such as restricting certain file types or removing certain words. Bing also uses a similar set of search operators to assist users in explicitly narrowing down the context of their queries. Bing allows users to search within a time range, by file type, by location, language, and more. There are other systems being developed that are working on automatically inferring the context of user queries based on the content of other documents they view or edit. IBM's Watson Project aims to create a cognitive technology that dynamically learns as it processes user queries. When presented with a query Watson creates a hypothesis that is evaluated against its present bank of knowledge based on previous questions. As related terms and relevant documents are matched against the query, Watson's hypothesis is modified to reflect the new information provided through unstructured data based on information it has obtained in previous situations. Watson's ability to build off previous knowledge allows queries to be automatically filtered for similar contexts in order to supply precise results.
Views: 393 The Audiopedia
Mathematical optimization model that helps with decision-making in uncertain situations
 
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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."
Linear Programming Problem (LPP) in R | Optimization | Operation Research
 
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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: 9813 Analytics University
What is CONSTRAINT GRAMMAR? What does CONSTRAINT GRAMMAR mean? CONSTRAINT GRAMMAR meaning
 
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What is CONSTRAINT GRAMMAR? What does CONSTRAINT GRAMMAR mean? CONSTRAINT GRAMMAR meaning - CONSTRAINT GRAMMAR definition - CONSTRAINT GRAMMAR explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Constraint Grammar (CG) is a methodological paradigm for natural language processing (NLP). Linguist-written, context dependent rules are compiled into a grammar that assigns grammatical tags ("readings") to words or other tokens in running text. Typical tags address lemmatisation (lexeme or base form), inflexion, derivation, syntactic function, dependency, valency, case roles, semantic type etc. Each rule either adds, removes, selects or replaces a tag or a set of grammatical tags in a given sentence context. Context conditions can be linked to any tag or tag set of any word anywhere in the sentence, either locally (defined distances) or globally (undefined distances). Context conditions in the same rule may be linked, i.e. conditioned upon each other, negated, or blocked by interfering words or tags. Typical CGs consist of thousands of rules, that are applied set-wise in progressive steps, covering ever more advanced levels of analysis. Within each level, safe rules are used before heuristic rules, and no rule is allowed to remove the last reading of a given kind, thus providing a high degree of robustness. The Constraint Grammar concept was launched by Fred Karlsson in 1990 (Karlsson 1990; Karlsson et al., eds, 1995), and CG taggers and parsers have since been written for a large variety of languages, routinely achieving accuracy F-scores for part of speech (word class) of over 99%. A number of syntactic CG systems have reported F-scores of around 95% for syntactic function labels. CG systems can be used to create full syntactic trees in other formalisms by adding small, non-terminal based phrase structure grammars or dependency grammars, and a number of Treebank projects have used Constraint Grammar for automatic annotation. CG methodology has also been used in a number of language technology applications, such as spell checkers and machine translation systems.
Views: 28 The Audiopedia
GOTO 2012 • Bottleneck Analysis • Adrian Cockcroft
 
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This presentation was recorded at GOTO Aarhus 2012 http://gotocon.com Adrian Cockcroft - Director of Architecture for the Cloud Systems Team at Netflix ABSTRACT It's hard to get served a drink at the bar during a conference. There are many times that you will be staring down the empty neck of your last beer bottle and feeling thirsty while you wait in line. There are some common problems that occur, but how can the conference organizers easily figure out which problem is constraining the throughput of this important function, and optimize quick delivery to thirsty attendees? https://twitter.com/gotocon https://www.facebook.com/GOTOConference http://gotocon.com
Views: 965 GOTO Conferences
Quantitative Techniques Course Plan
 
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Quantitative Analysis is research intensive and high level course in Statistics offered to graduate students of MS (MS) and MS(PM) during their second semester in COMSATS Attock Campus. The course aims to equip the students with advanced level contents in Methods, Evaluation & Developing Advanced Research Skills and Application of the Advanced Quantitative Analysis. The course follows intensively tools and techniques from inferential statistics to address variety of research problems using real world data and preparing publishing quality research articles. The course is specifically designed to enrich the understanding of students in how to develop a plan for selecting a real world research question, chose the appropriate sampling strategy, adopt a specific statistical approach, evaluate the question using the relevant statistical data through a sophisticated statistical softwares like Stata or SPSS, and interpret the results in formal academic styles of writing articles, theses and reports. This course also aims to raise the technical skills of students to next levels about data collection strategies, data analysis approaches, writing and presenting the results for academic and professional audience. Moreover, the course will use Stata, SPSS and SmartPLS/WarpPLS for all practical data analysis of each topic where needed. Developing the skills of quantitative analysis and usage of statistical softwares is thus the key objective of this course.
Lecture 6: Dependency Parsing
 
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Lecture 6 covers dependency parsing which is the task of analyzing the syntactic dependency structure of a given input sentence S. The output of a dependency parser is a dependency tree where the words of the input sentence are connected by typed dependency relations. Key phrases: Dependency Parsing. ------------------------------------------------------------------------------- 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/
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: 328 emnlp acl
Knowledge Graphs: The Path to Enterprise — Michael Moore and AI Omar Azhar, EY
 
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Michael Moore, Ph.D. — Executive Director, EY Performance Improvement Advisory, Enterprise Knowledge Graphs + AI Lead, EY and Omar Azhar, M.S. — Manager, EY Financial Services Organization Advisory, AI Strategy and Advanced Analytics COE, EY
Views: 3378 Neo4j
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: 1202 Aymeric Fouchault
Linear Programming decoder In NLP Part 1
 
01:29:06
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: 1309 emnlp acl
Graph neural networks: Variations and applications
 
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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: 6183 Microsoft Research
What is RESOURCE DESCRIPTION FRAMEWORK? What does RESOURCE DESCRIPTION FRAMEWORK mean?
 
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What is RESOURCE DESCRIPTION FRAMEWORK? What does RESOURCE DESCRIPTION FRAMEWORK mean? RESOURCE DESCRIPTION FRAMEWORK meaning - RESOURCE DESCRIPTION FRAMEWORK definition - RESOURCE DESCRIPTION FRAMEWORK explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. The Resource Description Framework (RDF) is a family of World Wide Web Consortium (W3C) specifications originally designed as a metadata data model. It has come to be used as a general method for conceptual description or modeling of information that is implemented in web resources, using a variety of syntax notations and data serialization formats. It is also used in knowledge management applications. RDF was adopted as a W3C recommendation in 1999. The RDF 1.0 specification was published in 2004, the RDF 1.1 specification in 2014. The RDF data model is similar to classical conceptual modeling approaches (such as entity–relationship or class diagrams). It is based upon the idea of making statements about resources (in particular web resources) expressions, known as triples. Triples are so named because they follow a subject–predicate–object structure. The subject denotes the resource, and the predicate denotes traits or aspects of the resource, and expresses a relationship between the subject and the object. For example, one way to represent the notion "The sky has the color blue" in RDF is as the triple: a subject denoting "the sky", a predicate denoting "has the color", and an object denoting "blue". Therefore, RDF swaps object for subject in contrast to the typical approach of an entity–attribute–value model in object-oriented design: entity (sky), attribute (color), and value (blue). RDF is an abstract model with several serialization formats (i.e. file formats), so the particular encoding for resources or triples varies from format to format. This mechanism for describing resources is a major component in the W3C's Semantic Web activity: an evolutionary stage of the World Wide Web in which automated software can store, exchange, and use machine-readable information distributed throughout the Web, in turn enabling users to deal with the information with greater efficiency and certainty. RDF's simple data model and ability to model disparate, abstract concepts has also led to its increasing use in knowledge management applications unrelated to Semantic Web activity. A collection of RDF statements intrinsically represents a labeled, directed multi-graph. This theoretically makes an RDF data model better suited to certain kinds of knowledge representation than other relational or ontological models. However, in practice, RDF data is often persisted in relational database or native representations (also called Triplestores—or Quad stores, if context (i.e. the named graph) is also persisted for each RDF triple). ShEX, or Shape Expressions, is a language for expressing constraints on RDF graphs. It includes the cardinality constraints from OSLC Resource Shapes and Dublin Core Description Set Profiles, as well as logical connectives for disjunction and polymorphism. As RDFS and OWL demonstrate, one can build additional ontology languages upon RDF.
Views: 1430 The Audiopedia
IJDKP
 
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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
Automatic Speech Recognition - An Overview
 
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An overview of how Automatic Speech Recognition systems work and some of the challenges. See more on this video at https://www.microsoft.com/en-us/research/video/automatic-speech-recognition-overview/
Views: 23320 Microsoft Research
Sherlock Is Garbage, And Here's Why
 
01:49:53
Why is Sherlock so bad? Harris Bomberguy is on the case! This version of the video has been slightly edited to get around the BBC's automatic video-blocking stuff. My Twitter: https://twitter.com/hbomberguy My Patreon: https://www.patreon.com/Hbomb CREDITS: Written by Harris Bomberguy and Sara Ghaleb Voiced + Edited by Harris Bomberguy Music: The Usual Incompetech The Final Fantasy Mystic Quest OST (it's a good game, shut up, I will destroy you) Passions Hi-Fi
Views: 2669451 hbomberguy
Semi-supervised Learning on Graphs, Using Observed Correlation Structure
 
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Art Owen, Stanford University Unifying Theory and Experiment for Large-Scale Networks http://simons.berkeley.edu/talks/art-owen-2013-11-20
Views: 538 Simons Institute
Christopher Manning - "Building Neural Network Models That Can Reason" (TCSDLS 2017-2018)
 
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Speaker: Christopher Manning, Thomas M. Siebel Professor in Machine Learning and Professor of Linguistics and of Computer Science, Stanford University Title: Building Neural Network Models That Can Reason Abstract: Deep learning has had enormous success on perceptual tasks but still struggles in providing a model for inference. To address this gap, we have been developing Memory-Attention-Composition networks (MACnets). The MACnet design provides a strong prior for explicitly iterative reasoning, enabling it to support explainable, structured learning, as well as good generalization from a modest amount of data. The model builds on the great success of existing recurrent cells such as LSTMs: A MacNet is a sequence of a single recurrent Memory, Attention, and Composition (MAC) cell. Its careful design imposes structural constraints on the operation of each cell and the interactions between them, incorporating explicit control and soft attention mechanisms into their interfaces. We demonstrate the model’s strength and robustness on the challenging CLEVR dataset for visual reasoning (Johnson et al. 2016), achieving a new state-of-the-art 98.9% accuracy, halving the error rate of the previous best model. More importantly, we show that the new model is more computationally efficient and data-efficient, requiring an order of magnitude less time and/or data to achieve good results. Joint work with Drew Hudson. Biography: Christopher Manning is the Thomas M. Siebel Professor in Machine Learning, Linguistics and Computer Science at Stanford University. He works on software that can intelligently process, understand, and generate human language material. He is a leader in applying Deep Learning to Natural Language Processing, including exploring Tree Recursive Neural Networks, sentiment analysis, neural network dependency parsing, the GloVe model of word vectors, neural machine translation, and deep language understanding. He also focuses on computational linguistic approaches to parsing, robust textual inference and multilingual language processing, including being a principal developer of Stanford Dependencies and Universal Dependencies. Manning is an ACM Fellow, a AAAI Fellow, an ACL Fellow, and a Past President of ACL. He has coauthored leading textbooks on statistical natural language processing and information retrieval. He is the founder of the Stanford NLP group (@stanfordnlp) and manages development of the Stanford CoreNLP software. cs.unc.edu/tcsdls
Views: 1438 UNC Computer Science
IJDKP
 
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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
Transportation problem [ MODI method - U V method - Optimal  Solution ] :-by kauserwise
 
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NOTE: Formula "pij = ui+vi-Cij" according to this formula the optimal values should be Zero or less than Zero which mean Zero or negative values, and in this formula if we did not reach the optimality then we should select the maximum positive value to proceed further. If you use this Cij-(u1+vj) formula then the values should be zero or positive value to reach the optimality, and in this formula if we did not reach the optimality then we should select the maximum negative value to proceed further. We can apply either any any one of the formula to find out the optimality. So both the formulas are doing same thing only but the values of sign (- +) will be differ. Here is the video about Transportation problem in Modi method-U V method using north west corner method, optimum solution in operation research, with sample problem in simple manner. Hope this will help you to get the subject knowledge at the end. Thanks and All the best. To watch more tutorials pls use this: www.youtube.com/c/kauserwise * Financial Accounts * Corporate accounts * Cost and Management accounts * Operations Research * Statistics ▓▓▓▓░░░░───CONTRIBUTION ───░░░▓▓▓▓ If you like this video and wish to contribute lls use Paytm. * Paytm a/c : 7401428918 [Every contribution is helpful] Thanks & All the Best!!! ───────────────────────────
Views: 1690710 Kauser Wise
Predictive Learning, NIPS 2016 | Yann LeCun, Facebook Research
 
56:53
Deep learning has been at the root of significant progress in many application areas, such as computer perception and natural language processing. But almost all of these systems currently use supervised learning with human-curated labels. The challenge of the next several years is to let machines learn from raw, unlabeled data, such as images, videos and text. Intelligent systems today do not possess "common sense", which humans and animals acquire by observing the world, acting in it, and understanding the physical constraints of it. I will argue that allowing machine to learn predictive models of the world is key to significant progress in artificial intelligence, and a necessary component of model-based planning and reinforcement learning. The main technical difficulty is that the world is only partially predictable. A general formulation of unsupervised learning that deals with partial predictability will be presented. The formulation connects many well-known approaches to unsupervised learning, as well as new and exciting ones such as adversarial training.
Views: 2500 Preserve Knowledge
UML Class Diagram Tutorial
 
10:17
Learn how to make classes, attributes, and methods in this UML Class Diagram tutorial. There's also in-depth training and examples on inheritance, aggregation, and composition relationships. UML (or Unified Modeling Language) is a software engineering language that was developed to create a standard way of visualizing the design of a system. And UML Class Diagrams describe the structure of a system by showing the system’s classes and how they relate to one another. This tutorial explains several characteristics of class diagrams. Within a class, there are attributes, methods, visibility, and data types. All of these components help identify a class and explain what it does. There are also several different types of relationships that exist within UML Class Diagrams. Inheritance is when a child class (or subclass) takes on all the attributes and methods of the parent class (or superclass). Association is a very basic relationship where there's no dependency. Aggregation is a relationship where the part can exist outside the whole. And finally, Composition is when a part cannot exist outside the whole. A class would be destroyed if the class it's related to is destroyed. Further UML Class Diagram information: https://www.lucidchart.com/pages/uml/class-diagram —— Learn more and sign up: http://www.lucidchart.com Follow us: Facebook: https://www.facebook.com/lucidchart Twitter: https://twitter.com/lucidchart Instagram: https://www.instagram.com/lucidchart LinkedIn: https://www.linkedin.com/company/lucidsoftware —— Credits for Photos with Attribution Requirements Tortoise - by Niccie King - http://bit.ly/2uHaL1G Otter - by Michael Malz - http://bit.ly/2vrVoYt Slow Loris - by David Haring - http://bit.ly/2uiBWxg Creep - by Poorna Kedar - http://bit.ly/2twR4K8 Visitor Center - by McGheiver - http://bit.ly/2uip0Hq Lobby - by cursedthing - http://bit.ly/2twBWw9
Views: 502252 Lucidchart
Proactive Learning and Structural Transfer Learning: Building Blocks of Cognitive Systems
 
28:45
Dr. Jaime Carbonell is an expert in machine learning, scalable data mining (“big data”), text mining, machine translation, and computational proteomics. He invented Proactive Machine Learning, including its underlying decision-theoretic framework, and new Transfer Learning methods. He is also known for the Maximal Marginal Relevance principle in information retrieval. Dr. Carbonell has published some 350 papers and books and supervised 65 Ph.D. dissertations. He has served on multiple governmental advisory committees, including the Human Genome Committee of the National Institutes of Health, and is Director of the Language Technologies Institute. At CMU, Dr. Carbonell has designed degree programs and courses in language technologies, machine learning, data sciences, and electronic commerce. He received his Ph.D. from Yale University. For more, read the white paper, "Computing, cognition, and the future of knowing" https://ibm.biz/BdHErb
Views: 1704 IBM Research
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
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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: 3 aircc 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: 48 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: 159 ijdkp jou
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 )
 
00:10
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: 3 aircc journal
Multi view Partitioning via Tensor Methods
 
00:59
Gagner Technologies offers M.E projects based on IEEE 2013 . Final Year Projects, M.E projects 2013-2014, mini projects 2013-2014, Real Time Projects, Final Year Projects for BE ECE, CSE, IT, MCA, B TECH, ME, M SC (IT), BCA, BSC CSE, IT IEEE 2013 Projects in Data Mining, Distributed System, Mobile Computing, Networks, Networking. IEEE 2013 - 2014 projects. Final Year Projects at Chennai, IEEE Software Projects, Engineering Projects, MCA projects, BE projects, JAVA projects, J2EE projects, .NET projects, Students projects, Final Year Student Projects, IEEE Projects 2013-2014, Real Time Projects, Final Year Projects for BE ECE, CSE, IT, MCA, B TECH, ME, M SC (IT), BCA, BSC CSE, IT, Contact: Gagner Technologies No.7 Police quarters Road, T.Nagar (Behind T.Nagar Bus Stand),Chennai-600017, call 8680939422,04424320908 www.gagner.in mail: [email protected]
16. Learning: Support Vector Machines
 
49:34
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In this lecture, we explore support vector machines in some mathematical detail. We use Lagrange multipliers to maximize the width of the street given certain constraints. If needed, we transform vectors into another space, using a kernel function. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 675588 MIT OpenCourseWare
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: 22 aircc journal
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: 19 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: 45 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: 33 aircc journal
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: 144 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: 28 aircc journal
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
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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: 19 aircc journal
Crowdsourcing: Achieving Data Quality with Imperfect Humans
 
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Crowdsourcing is a great tool to collect data and support machine learning -- it is the ultimate form of outsourcing. But crowdsourcing introduces budget and quality challenges that must be addressed to realize its benefits. In this talk, Panos Ipeirotis of New York University, will discuss the use of crowdsourcing for building robust machine learning models quickly and under budget constraints. Operating under the realistic assumption that we are processing imperfect labels that reflect random and systematic error on the part of human workers, he will also describe how our "beat the machine" system engages humans. This will improve a machine learning system by discovering cases where the machine fails and fails while confident on being correct.Finally, he will discuss latest results showing that mice and Mechanical Turk workers are not that different after all. Panos Ipeirotis, New York University 10/23/2012
Views: 131 UWTV
An Introduction to Temporal Databases
 
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Check out http://www.pgconf.us/2015/event/83/ for the full talk details. 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: 4311 Postgres Conference
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
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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: 13 aircc journal
International Journal of Data Mining & Knowledge Management Process  IJDKP
 
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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.
ALIEN 2.0: The Infinite Memory
 
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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: 253 Federico Pernici
LIVE: Confirmation hearing for Supreme Court nominee Judge Brett Kavanaugh (Day 2)
 
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Confirmation hearing for Supreme Court nominee Judge Brett #Kavanaugh (Day 2, PArt 1) - LIVE at 9:30am ET on C-SPAN3, C-SPAN Radio & online here: https://cs.pn/2NRS3KW
Views: 117793 C-SPAN
Out of the Fiery Furnace - Episode 1 - From Stone to Bronze
 
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From the Stone Age to the era of the silicon chip — metals and minerals have marked the milestones of our civilization. OUT OF THE FIERY FURNACE traces the story of civilization through the exploitation of metals, minerals and energy resources. Renowned radio and BBC television commentator Michael Charlton hosts seven, one-hour programs filmed in more than 50 different parts of the world. This very unusual public television series combines the disciplines of history, science, archeology and economics in order to explore the relationship between technology and society. How did human beings first come to recognize metals buried in rocks? Michael Charlton visits an archaeological dig at a Stone Age settlement to uncover the ways in which our early ancestors extracted metal from rock. This episode visits several dramatic locations, including India and the Sinai Desert to follow remarkable experiments using the smelting techniques of the ancient civilizations. You'll also travel to Thailand to find a possible answer to a great mystery: how did bronze come to be invented in the Middle East where there are no deposits of a necessary element — tin? (60 minutes) VHS Cover: http://i.imgur.com/RuPFqrt Disclaimer: This video series, produced in 1986 by Opus Films is shown here for Educational Purposes. It includes footage of cultures in India, China, Near East, etc. and ancient methods of manufacturing metals. It is hoped that this information is useful for archival and educational purposes to viewers all across the world. The video is provided here under the Fair Use policy.