Computational Learning Theory and Natural Learning Systems: Making learning systems practical

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Publisher : MIT Press
ISBN 13 : 9780262571180
Total Pages : 440 pages
Book Rating : 4.5/5 (711 download)

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Book Synopsis Computational Learning Theory and Natural Learning Systems: Making learning systems practical by : Russell Greiner

Download or read book Computational Learning Theory and Natural Learning Systems: Making learning systems practical written by Russell Greiner and published by MIT Press. This book was released on 1994 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and Ǹatural' Learning Systems." The purpose of the workshops was to explore the emerging intersection of theoretical learning research and natural learning systems. The workshops drew researchers from three historically distinct styles of learning research: computational learning theory, neural networks, and machine learning (a subfield of AI). Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems. Contributors : Klaus Abraham-Fuchs, Yasuhiro Akiba, Hussein Almuallim, Arunava Banerjee, Sanjay Bhansali, Alvis Brazma, Gustavo Deco, David Garvin, Zoubin Ghahramani, Mostefa Golea, Russell Greiner, Mehdi T. Harandi, John G. Harris, Haym Hirsh, Michael I. Jordan, Shigeo Kaneda, Marjorie Klenin, Pat Langley, Yong Liu, Patrick M. Murphy, Ralph Neuneier, E.M. Oblow, Dragan Obradovic, Michael J. Pazzani, Barak A. Pearlmutter, Nageswara S.V. Rao, Peter Rayner, Stephanie Sage, Martin F. Schlang, Bernd Schurmann, Dale Schuurmans, Leon Shklar, V. Sundareswaran, Geoffrey Towell, Johann Uebler, Lucia M. Vaina, Takefumi Yamazaki, Anthony M. Zador.

Computational Learning Theory and Natural Learning Systems: Intersections between theory and experiment

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Publisher : Mit Press
ISBN 13 : 9780262581332
Total Pages : 449 pages
Book Rating : 4.5/5 (813 download)

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Book Synopsis Computational Learning Theory and Natural Learning Systems: Intersections between theory and experiment by : Stephen José Hanson

Download or read book Computational Learning Theory and Natural Learning Systems: Intersections between theory and experiment written by Stephen José Hanson and published by Mit Press. This book was released on 1994 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: Annotation These original contributions converge on an exciting and fruitful intersection of three historically distinct areas of learning research: computational learning theory, neural networks, and symbolic machine learning. Bridging theory and practice, computer science and psychology, they consider general issues in learning systems that could provide constraints for theory and at the same time interpret theoretical results in the context of experiments with actual learning systems. In all, nineteen chapters address questions such as, What is a natural system? How should learning systems gain from prior knowledge? If prior knowledge is important, how can we quantify how important? What makes a learning problem hard? How are neural networks and symbolic machine learning approaches similar? Is there a fundamental difference in the kind of task a neural network can easily solve as opposed to those a symbolic algorithm can easily solve? Stephen J. Hanson heads the Learning Systems Department at Siemens Corporate Research and is a Visiting Member of the Research Staff and Research Collaborator at the Cognitive Science Laboratory at Princeton University. George A. Drastal is Senior Research Scientist at Siemens Corporate Research. Ronald J. Rivest is Professor of Computer Science and Associate Director of the Laboratory for Computer Science at the Massachusetts Institute of Technology.

Computational Learning Theory and Natural Learning Systems

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Author :
Publisher : Mit Press
ISBN 13 : 9780262581332
Total Pages : 449 pages
Book Rating : 4.5/5 (813 download)

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Book Synopsis Computational Learning Theory and Natural Learning Systems by : Stephen José Hanson

Download or read book Computational Learning Theory and Natural Learning Systems written by Stephen José Hanson and published by Mit Press. This book was released on 1994 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: As with Volume I, this second volume represents a synthesis of issues in three historically distinct areas of learning research: computational learning theory, neural network research, and symbolic machine learning. While the first volume provided a forum for building a science of computational learning across fields, this volume attempts to define plausible areas of joint research: the contributions are concerned with finding constraints for theory while at the same time interpreting theoretic results in the context of experiments with actual learning systems. Subsequent volumes will focus on areas identified as research opportunities.Computational learning theory, neural networks, and AI machine learning appear to be disparate fields; in fact they have the same goal: to build a machine or program that can learn from its environment. Accordingly, many of the papers in this volume deal with the problem of learning from examples. In particular, they are intended to encourage discussion between those trying to build learning algorithms (for instance, algorithms addressed by learning theoretic analyses are quite different from those used by neural network or machine-learning researchers) and those trying to analyze them.The first section provides theoretical explanations for the learning systems addressed, the second section focuses on issues in model selection and inductive bias, the third section presents new learning algorithms, the fourth section explores the dynamics of learning in feedforward neural networks, and the final section focuses on the application of learning algorithms.A Bradford Book

Computational Learning Theory and Natural Learning Systems: Selecting good models

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Publisher : Bradford Books
ISBN 13 :
Total Pages : 448 pages
Book Rating : 4.:/5 (318 download)

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Book Synopsis Computational Learning Theory and Natural Learning Systems: Selecting good models by : Stephen José Hanson

Download or read book Computational Learning Theory and Natural Learning Systems: Selecting good models written by Stephen José Hanson and published by Bradford Books. This book was released on 1994 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems.

Computational Learning Theory and Natural Learning Systems

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Publisher :
ISBN 13 :
Total Pages : 407 pages
Book Rating : 4.:/5 (946 download)

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Book Synopsis Computational Learning Theory and Natural Learning Systems by : Thomas Petsche

Download or read book Computational Learning Theory and Natural Learning Systems written by Thomas Petsche and published by . This book was released on 1997 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt:

The Mathematics Of Generalization

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Publisher : CRC Press
ISBN 13 : 0429961073
Total Pages : 460 pages
Book Rating : 4.4/5 (299 download)

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Book Synopsis The Mathematics Of Generalization by : David. H Wolpert

Download or read book The Mathematics Of Generalization written by David. H Wolpert and published by CRC Press. This book was released on 2018-03-05 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides different mathematical frameworks for addressing supervised learning. It is based on a workshop held under the auspices of the Center for Nonlinear Studies at Los Alamos and the Santa Fe Institute in the summer of 1992.

Goal-driven Learning

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Publisher : MIT Press
ISBN 13 : 9780262181655
Total Pages : 548 pages
Book Rating : 4.1/5 (816 download)

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Book Synopsis Goal-driven Learning by : Ashwin Ram

Download or read book Goal-driven Learning written by Ashwin Ram and published by MIT Press. This book was released on 1995 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This book brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. It collects and solidifies existing results on this important issue in machine and human learning and presents a theoretical framework for future investigations. The book opens with an an overview of goal-driven learning research and computational and cognitive models of the goal-driven learning process. This introduction is followed by a collection of fourteen recent research articles addressing fundamental issues of the field, including psychological and functional arguments for modeling learning as a deliberative, planful process; experimental evaluation of the benefits of utility-based analysis to guide decisions about what to learn; case studies of computational models in which learning is driven by reasoning about learning goals; psychological evidence for human goal-driven learning; and the ramifications of goal-driven learning in educational contexts. The second part of the book presents six position papers reflecting ongoing research and current issues in goal-driven learning. Issues discussed include methods for pursuing psychological studies of goal-driven learning, frameworks for the design of active and multistrategy learning systems, and methods for selecting and balancing the goals that drive learning. A Bradford Book

Foundations of Machine Learning, second edition

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Publisher : MIT Press
ISBN 13 : 0262351366
Total Pages : 505 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Foundations of Machine Learning, second edition by : Mehryar Mohri

Download or read book Foundations of Machine Learning, second edition written by Mehryar Mohri and published by MIT Press. This book was released on 2018-12-25 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.

Encyclopedia of Information Systems and Technology - Two Volume Set

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Publisher : CRC Press
ISBN 13 : 1000031748
Total Pages : 1307 pages
Book Rating : 4.0/5 ( download)

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Book Synopsis Encyclopedia of Information Systems and Technology - Two Volume Set by : Phillip A. Laplante

Download or read book Encyclopedia of Information Systems and Technology - Two Volume Set written by Phillip A. Laplante and published by CRC Press. This book was released on 2015-12-29 with total page 1307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spanning the multi-disciplinary scope of information technology, the Encyclopedia of Information Systems and Technology draws together comprehensive coverage of the inter-related aspects of information systems and technology. The topics covered in this encyclopedia encompass internationally recognized bodies of knowledge, including those of The IT BOK, the Chartered Information Technology Professionals Program, the International IT Professional Practice Program (British Computer Society), the Core Body of Knowledge for IT Professionals (Australian Computer Society), the International Computer Driving License Foundation (European Computer Driving License Foundation), and the Guide to the Software Engineering Body of Knowledge. Using the universally recognized definitions of IT and information systems from these recognized bodies of knowledge, the encyclopedia brings together the information that students, practicing professionals, researchers, and academicians need to keep their knowledge up to date. Also Available Online This Taylor & Francis encyclopedia is also available through online subscription, offering a variety of extra benefits for researchers, students, and librarians, including: Citation tracking and alerts Active reference linking Saved searches and marked lists HTML and PDF format options Contact Taylor and Francis for more information or to inquire about subscription options and print/online combination packages. US: (Tel) 1.888.318.2367; (E-mail) [email protected] International: (Tel) +44 (0) 20 7017 6062; (E-mail) [email protected]

Inductive Logic Programming

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Publisher : Springer
ISBN 13 : 3540487514
Total Pages : 308 pages
Book Rating : 4.5/5 (44 download)

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Book Synopsis Inductive Logic Programming by : Saso Dzeroski

Download or read book Inductive Logic Programming written by Saso Dzeroski and published by Springer. This book was released on 2003-06-26 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 9th International Conference on Inductive Logic Programming, ILP-99, held in Bled, Slovenia, in June 1999. The 24 revised papers presented were carefully reviewed and selected from 40 submissions. Also included are abstracts of three invited contributions. The papers address all current issues in inductive logic programming and inductive learning, from foundational and methodological issues to applications, e.g. in natural language processing, knowledge discovery, and data mining.

Inductive Logic Programming

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Publisher : Springer Science & Business Media
ISBN 13 : 3540661093
Total Pages : 308 pages
Book Rating : 4.5/5 (46 download)

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Book Synopsis Inductive Logic Programming by : Sašo Džeroski

Download or read book Inductive Logic Programming written by Sašo Džeroski and published by Springer Science & Business Media. This book was released on 1999-06-09 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wewishtothank AlfredHofmannandAnnaKramerofSpringer-Verlagfortheircooperationin publishing these proceedings. Finally, we gratefully acknowledge the nancial supportprovidedbythesponsorsofILP-99.

Theoretical Advances in Neural Computation and Learning

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Publisher : Springer Science & Business Media
ISBN 13 : 1461526965
Total Pages : 482 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis Theoretical Advances in Neural Computation and Learning by : Vwani Roychowdhury

Download or read book Theoretical Advances in Neural Computation and Learning written by Vwani Roychowdhury and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: For any research field to have a lasting impact, there must be a firm theoretical foundation. Neural networks research is no exception. Some of the founda tional concepts, established several decades ago, led to the early promise of developing machines exhibiting intelligence. The motivation for studying such machines comes from the fact that the brain is far more efficient in visual processing and speech recognition than existing computers. Undoubtedly, neu robiological systems employ very different computational principles. The study of artificial neural networks aims at understanding these computational prin ciples and applying them in the solutions of engineering problems. Due to the recent advances in both device technology and computational science, we are currently witnessing an explosive growth in the studies of neural networks and their applications. It may take many years before we have a complete understanding about the mechanisms of neural systems. Before this ultimate goal can be achieved, an swers are needed to important fundamental questions such as (a) what can neu ral networks do that traditional computing techniques cannot, (b) how does the complexity of the network for an application relate to the complexity of that problem, and (c) how much training data are required for the resulting network to learn properly? Everyone working in the field has attempted to answer these questions, but general solutions remain elusive. However, encouraging progress in studying specific neural models has been made by researchers from various disciplines.

Algorithmic Learning Theory

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Publisher : Springer
ISBN 13 : 3540316965
Total Pages : 502 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Algorithmic Learning Theory by : Sanjay Jain

Download or read book Algorithmic Learning Theory written by Sanjay Jain and published by Springer. This book was released on 2005-10-11 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 16th International Conference on Algorithmic Learning Theory, ALT 2005, held in Singapore in October 2005. The 30 revised full papers presented together with 5 invited papers and an introduction by the editors were carefully reviewed and selected from 98 submissions. The papers are organized in topical sections on kernel-based learning, bayesian and statistical models, PAilearning, query-learning, inductive inference, language learning, learning and logic, learning from expert advice, online learning, defensive forecasting, and teaching.

Computational Learning Theory and Natural Learning Systems

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Publisher :
ISBN 13 :
Total Pages : 550 pages
Book Rating : 4.:/5 (93 download)

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Book Synopsis Computational Learning Theory and Natural Learning Systems by : Stephen José Hanson

Download or read book Computational Learning Theory and Natural Learning Systems written by Stephen José Hanson and published by . This book was released on 1994 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt:

ECAI 2000

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Publisher :
ISBN 13 : 9784274903885
Total Pages : 796 pages
Book Rating : 4.9/5 (38 download)

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Book Synopsis ECAI 2000 by : Werner Horn

Download or read book ECAI 2000 written by Werner Horn and published by . This book was released on 2000 with total page 796 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Computational Learning Theory

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Publisher : Springer Science & Business Media
ISBN 13 : 9783540626855
Total Pages : 350 pages
Book Rating : 4.6/5 (268 download)

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Book Synopsis Computational Learning Theory by : Shai Ben-David

Download or read book Computational Learning Theory written by Shai Ben-David and published by Springer Science & Business Media. This book was released on 1997-03-03 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Content Description #Includes bibliographical references and index.

Handbook of Natural Language Processing

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Publisher : CRC Press
ISBN 13 : 9780824790004
Total Pages : 974 pages
Book Rating : 4.7/5 (9 download)

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Book Synopsis Handbook of Natural Language Processing by : Robert Dale

Download or read book Handbook of Natural Language Processing written by Robert Dale and published by CRC Press. This book was released on 2000-07-25 with total page 974 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study explores the design and application of natural language text-based processing systems, based on generative linguistics, empirical copus analysis, and artificial neural networks. It emphasizes the practical tools to accommodate the selected system.