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Titlebook: The Nature of Statistical Learning Theory; Vladimir N. Vapnik Book 2000Latest edition Springer Science+Business Media New York 2000 Condit

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發(fā)表于 2025-3-21 19:52:28 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱The Nature of Statistical Learning Theory
編輯Vladimir N. Vapnik
視頻videohttp://file.papertrans.cn/915/914610/914610.mp4
概述The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization..It considers learning as a general problem of function estimation based
叢書(shū)名稱Information Science and Statistics
圖書(shū)封面Titlebook: The Nature of Statistical Learning Theory;  Vladimir N. Vapnik Book 2000Latest edition Springer Science+Business Media New York 2000 Condit
描述The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: * the setting of learning problems based on the model of minimizing the risk functional from empirical data * a comprehensive analysis of the empirical risk minimization principle including necessary and sufficient conditions for its consistency * non-asymptotic bounds for the risk achieved using the empirical risk minimization principle * principles for controlling the generalization ability of learning machines using small sample sizes based on these bounds * the Support Vector methods that control the generalization ability when estimating function using small sample size. The second edition of the book contains three new chapters devoted to further development of the learning theory and SVM techniques. These include: * the theory of direct method of learning based on solving m
出版日期Book 2000Latest edition
關(guān)鍵詞Conditional probability; Statistical Learning; Statistical Theory; cognition; control; learning; pattern r
版次2
doihttps://doi.org/10.1007/978-1-4757-3264-1
isbn_softcover978-1-4419-3160-3
isbn_ebook978-1-4757-3264-1Series ISSN 1613-9011 Series E-ISSN 2197-4128
issn_series 1613-9011
copyrightSpringer Science+Business Media New York 2000
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978-1-4419-3160-3Springer Science+Business Media New York 2000
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Information Science and Statisticshttp://image.papertrans.cn/t/image/914610.jpg
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1613-9011 s learning as a general problem of function estimation basedThe aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and
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Book 2000Latest editions that control the generalization ability when estimating function using small sample size. The second edition of the book contains three new chapters devoted to further development of the learning theory and SVM techniques. These include: * the theory of direct method of learning based on solving m
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