Computational Learning Theory, David Helmbold; Bob Williamson

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Цена: 69870.00 T
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Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

Автор: Kevin Murphy
Название: Machine Learning
ISBN: 0262018020 ISBN-13(EAN): 9780262018029
Издательство: MIT Press
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Цена: 124150.00 T
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Описание:

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.

The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Автор: Naoki Abe; Roni Khardon; Thomas Zeugmann
Название: Algorithmic Learning Theory
ISBN: 3540428755 ISBN-13(EAN): 9783540428756
Издательство: Springer
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Цена: 69870.00 T
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Описание: This volume contains the papers presented at the 12th Annual Conference on Algorithmic Learning Theory (ALT 2001), which was held in Washington DC, USA, during November 25-28, 2001. Cohen of the University of Massachusetts at Amherst, USA, and the joint invited talk for ALT 2001 and DS 2001 presented by Setsuo Arikawa of Kyushu University, Japan.

Автор: Hiroki Arimura; Sanjay Jain; Arun Sharma
Название: Algorithmic Learning Theory
ISBN: 3540412379 ISBN-13(EAN): 9783540412373
Издательство: Springer
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Цена: 65210.00 T
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Описание: These papers on algorithmic learning theory are organized in topical sections on statistical learning, inductive logic programming, inductive inference, complexity, neural networks and other paradigms, support vector machines.

Автор: Stephen J. Hanson; Werner Remmele; Ronald L. Rives
Название: Machine Learning: From Theory to Applications
ISBN: 3540564837 ISBN-13(EAN): 9783540564836
Издательство: Springer
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Описание: Containing key research papers which have been produced recently by the Massachusetts Institute of Technology and the Siemens corporation, this volume explores the theory of machine learning, artificial intelligence and symbolic learning methods, and neural and collective computation.

Автор: Setsuo Arikawa; Arun K. Sharma
Название: Algorithmic Learning Theory
ISBN: 3540618635 ISBN-13(EAN): 9783540618638
Издательство: Springer
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Цена: 74530.00 T
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Описание: Covering all areas related to algorithmic learning theory (ALT), ranging from theoretical foundations of machine learning to applications in several areas, this text presents papers from a workshop held on ALT in Sydney, in October 1996.

Автор: Osamu Watanabe; Takashi Yokomori
Название: Algorithmic Learning Theory
ISBN: 3540667482 ISBN-13(EAN): 9783540667483
Издательство: Springer
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Цена: 74530.00 T
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Описание: ThisvolumecontainsallthepaperspresentedattheInternationalConferenceon Algorithmic Learning Theory 1999 (ALT'99), held at Waseda University Int- nationalConferenceCenter, Tokyo, Japan, December 6?8,1999.Theconference was sponsored by the Japanese Society for Arti cial Intelligence (JSAI). In response to the call for papers, 51 papers on all aspects of algorithmic learning theory and related areas were submitted, of which 26 papers were - lected for presentation by the program committee based on their originality, quality, and relevance to the theory of machine learning. In addition to these regular papers, this volume contains three papers of invited lectures presented byKatharinaMorikoftheUniversityofDortmund, RobertE.SchapireofAT&T Labs, Shannon Lab., and Kenji Yamanishi of NEC, C&C Media Research Lab. ALT'99 is not just one of the ALT conference series, but this conference marks the tenth anniversary in the series that was launched in Tokyo, in Oc- ber 1990, for the discussion of research topics on all areas related to algorithmic learning theory. The ALT series was renamedlast year from\ALT workshop"to \ALT conference", expressing its wider goalof providing an ideal forum to bring together researchers from both theoretical and practical learning communities, producing novel concepts and criteria that would bene t both. This movement wasre?ectedinthepaperspresentedatALT'99, wheretherewereseveralpapers motivated by application oriented problems such as noise, data precision, etc. Furthermore, ALT'99 benet ed from being held jointly with the 2nd Inter- tional Conference on Discovery Science (DS'99), the conference for discussing, among other things, more applied aspects of machine learning. Also, we could celebrate the tenth anniversary of the ALT series with researchers from both theoretical and practical communities.

Автор: Vladimir Vapnik
Название: The Nature of Statistical Learning Theory
ISBN: 1441931600 ISBN-13(EAN): 9781441931603
Издательство: Springer
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Цена: 139750.00 T
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Описание: The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics.

Автор: Ming Li; Akira Maruoka
Название: Algorithmic Learning Theory
ISBN: 3540635777 ISBN-13(EAN): 9783540635772
Издательство: Springer
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Цена: 83850.00 T
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Описание: This proceedings volume presents 26 revised full papers and three invited papers. Among the topics addressed are PAC learning, learning algorithms, inductive learning, inductive inference, decision procedures, language learning, and game theoretical aspects of computational learning theory.

Автор: Anthony
Название: Computational Learning Theory
ISBN: 0521599229 ISBN-13(EAN): 9780521599221
Издательство: Cambridge Academ
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Цена: 46470.00 T
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Описание: This is a self contained volume in which the authors concentrate on the `probably approximately correct model`. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.

Автор: Jyrki Kivinen; Robert H. Sloan
Название: Computational Learning Theory
ISBN: 354043836X ISBN-13(EAN): 9783540438366
Издательство: Springer
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Цена: 81050.00 T
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Описание: Constitutes the proceedings of the 15th Annual Conference on Computational Learning Theory, held in Australia in 2002. The 26 papers cover statistical learning theory, online learning, inductive inference, PAC learning, boosting and other learning paradigms.

Автор: Paul Vitanyi
Название: Computational Learning Theory
ISBN: 3540591192 ISBN-13(EAN): 9783540591191
Издательство: Springer
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Описание: This volume of conference proceedings explores the computational aspects of artificial and natural learning systems and machine learning. Key issues discussed include neural networks, genetic algorithms, robotics, pattern recognition, decision theory and cryptography.