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The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning from the general point of view 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 general setting of learning problems and the general model of minimizing the risk functional from empirical data - a comprehensive analysis of the empirical risk minimization principle and shows how this allows for the construction of necessary and sufficient conditions for 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 - introducing a new type of universal learning machine that controls the generalization ability.
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"This interesting book helps a reader to understand the interconnections between various streams in the empirical modeling realm and may be recommended to any reader who feels lost in modern terminology." V.V. Fedorov, Oak Ridge National Laboratory, USA
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- PublisherSpringer
- Publication date1999
- ISBN 10 0387945598
- ISBN 13 9780387945590
- BindingHardcover
- Edition number1
- Number of pages188
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THE NATURE OF STATISTICAL LEARNING THEORY
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Springer-Verlag New York Inc.
(2000)
ISBN 10: 0387945598
ISBN 13: 9780387945590
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Book Description Condition: Sehr gut. Zustand: Sehr gut - Gepflegter, sauberer Zustand. 1. Auflage. Aus der Auflösung einer renommierten Bibliothek. Kann Stempel beinhalten. | Seiten: 203 | Sprache: Englisch. Seller Inventory # 41818288/202
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