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Algorithmic Learning Theory, Osamu Watanabe; Takashi Yokomori


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Автор: Osamu Watanabe; Takashi Yokomori
Название:  Algorithmic Learning Theory
ISBN: 9783540667483
Издательство: Springer
Классификация:
ISBN-10: 3540667482
Обложка/Формат: Paperback
Страницы: 372
Вес: 0.54 кг.
Дата издания: 17.11.1999
Серия: Lecture Notes in Artificial Intelligence
Язык: English
Размер: 234 x 156 x 20
Основная тема: Computer Science
Подзаголовок: 10th International Conference, ALT '99 Tokyo, Japan, December 6-8, 1999 Proceedings
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: ThisvolumecontainsallthepaperspresentedattheInternationalConferenceon Algorithmic Learning Theory 1999 (ALT99), 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. ALT99 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 workshopto \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?ectedinthepaperspresentedatALT99, wheretherewereseveralpapers motivated by application oriented problems such as noise, data precision, etc. Furthermore, ALT99 benet ed from being held jointly with the 2nd Inter- tional Conference on Discovery Science (DS99), 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.

Algorithmic Learning Theory

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.

Algorithmic Learning Theory

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.

Computational Learning Theory

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.

Principles and Theory for Data Mining and Machine Learning

Автор: Clarke
Название: Principles and Theory for Data Mining and Machine Learning
ISBN: 0387981349 ISBN-13(EAN): 9780387981345
Издательство: Springer
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Цена: 186330.00 T
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Описание: Extensive treatment of the most up-to-date topicsProvides the theory and concepts behind popular and emerging methodsRange of topics drawn from Statistics, Computer Science, and Electrical Engineering

Algorithmic Learning Theory

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.

Computational Learning Theory

Автор: Shai Ben-David
Название: Computational Learning Theory
ISBN: 3540626859 ISBN-13(EAN): 9783540626855
Издательство: Springer
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Цена: 65210.00 T
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Описание: This volume presents 25 revised full papers selected from a total of 36 submissions to the Third European Conference on Computational Learning Theory, EuroCOLT `97. It spans the whole spectrum of computational learning theory, with an emphasis on mathematical models of machine learning.

Algorithmic Learning Theory

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.

A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems

Название: A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
ISBN: 1849968675 ISBN-13(EAN): 9781849968676
Издательство: Springer
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Цена: 156720.00 T
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Описание: How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.

The Nature of Statistical Learning Theory

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.


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