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The Nature of Statistical Learning Theory - V. N. Vapnik
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ISBN: 9780387987804

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The Nature of Statistical Learning Theory 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 multidimensional integral equations for density, conditional probability, and conditional density estimation* a new inductive principle of learning.Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.Vladimir N. Vapnik is Technology Leader AT&T Labs-Research and Professor of London University. He is one of the founders of statistical learning theory, and the author of seven books published in English, Russian, German, and Chinese. Bücher / Fremdsprachige Bücher / Englische Bücher / Schule & Lernen 978-0-387-98780-4, Springer

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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 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 multidimensional integral equations for density, conditional probability, and conditional density estimation * a new inductive principle of learning. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists. Vladimir N. Vapnik is Technology Leader AT&T Labs-Research and Professor of London University. He is one of the founders of statistical learning theory, and the author of seven books published in English, Russian, German, andChinese. The Nature of Statistical Learning Theory Vapnik, Vladimir Naumovich / Vapnik, V. N., Springer

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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 as a general problem of function estimationbased 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 multidimensional integral equations for density, conditional probability, and conditional density estimation a new inductive principle of learning. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists. Buch / Mathematik, Naturwissenschaft & Technik / Mathematik / Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik, [PU: Springer]

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[ED: Buch], [PU: Springer-Verlag GmbH], Neuware - 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. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists., [SC: 0.00]

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2000, ISBN: 0387987800

ID: A2867067

2nd ed. Gebundene Ausgabe Lernen / Lerntheorie, Lernforschung, Statistik, COMPUTERS / Computer Science, MATHEMATICS / Probability & Statistics / General, mit Schutzumschlag neu, [PU:Springer-Verlag GmbH]

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Details zum Buch
The Nature of Statistical Learning Theory

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. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

Detailangaben zum Buch - The Nature of Statistical Learning Theory


EAN (ISBN-13): 9780387987804
ISBN (ISBN-10): 0387987800
Gebundene Ausgabe
Erscheinungsjahr: 2000
Herausgeber: Springer-Verlag GmbH
314 Seiten
Gewicht: 0,590 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 03.06.2007 09:22:50
Buch zuletzt gefunden am 11.03.2017 10:39:09
ISBN/EAN: 9780387987804

ISBN - alternative Schreibweisen:
0-387-98780-0, 978-0-387-98780-4


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