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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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: Hiroki Arimura; Sanjay Jain; Arun Sharma
Название: Algorithmic Learning Theory
ISBN: 3540412379 ISBN-13(EAN): 9783540412373
Издательство: Springer
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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: Setsuo Arikawa; Arun K. Sharma
Название: Algorithmic Learning Theory
ISBN: 3540618635 ISBN-13(EAN): 9783540618638
Издательство: Springer
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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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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: Naoki Abe; Roni Khardon; Thomas Zeugmann
Название: Algorithmic Learning Theory
ISBN: 3540428755 ISBN-13(EAN): 9783540428756
Издательство: Springer
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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: 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: Ming Li; Akira Maruoka
Название: Algorithmic Learning Theory
ISBN: 3540635777 ISBN-13(EAN): 9783540635772
Издательство: Springer
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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: Vladimir Vapnik
Название: The Nature of Statistical Learning Theory
ISBN: 1441931600 ISBN-13(EAN): 9781441931603
Издательство: Springer
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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: Paul Fischer; Hans U. Simon
Название: Computational Learning Theory
ISBN: 3540657010 ISBN-13(EAN): 9783540657019
Издательство: Springer
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Описание: This text presents the proceedings of the 4th European Conference on Computational Learning Theory. The 23 contributions address topics such as learning from queries and counter examples, reinforcement learning, online learning and export advice, teaching and learning and inductive inference.

Автор: Anthony
Название: Computational Learning Theory
ISBN: 0521599229 ISBN-13(EAN): 9780521599221
Издательство: Cambridge Academ
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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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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.