Articles liés à The Nature of Statistical Learning Theory
Synopsis
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. Written in readable and concise style and devoted to key learning problems, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
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- ÉditeurSpringer
- Date d'édition2010
- ISBN 10 1441931600
- ISBN 13 9781441931603
- ReliureBroché
- Langueanglais
- Numéro d'édition2
- Nombre de pages336
- Coordonnées du fabricantnon disponible
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The Nature of Statistical Learning Theory
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Springer New York, 2010
ISBN 10 : 1441931600
ISBN 13 : 9781441931603
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. Written in readable and concise style and devoted to key learning problems, the book is intended for statisticians, mathematicia. N° de réf. du vendeur 4173616
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The Nature of Statistical Learning Theory (Information Science and Statistics)
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The Nature of Statistical Learning Theory
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Springer New York Dez 2010, 2010
ISBN 10 : 1441931600
ISBN 13 : 9781441931603
Neuf
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - 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. 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 336 pp. Englisch. N° de réf. du vendeur 9781441931603
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Nature of Statistical Learning Theory
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Nature of Statistical Learning Theory
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The Nature of Statistical Learning Theory
Edité par
Springer New York, Springer US, 2010
ISBN 10 : 1441931600
ISBN 13 : 9781441931603
Neuf
Taschenbuch
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9781441931603
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The Nature of Statistical Learning Theory
Edité par
Springer New York, Springer US Dez 2010, 2010
ISBN 10 : 1441931600
ISBN 13 : 9781441931603
Neuf
Taschenbuch
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. 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. 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 ofSpringer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 336 pp. Englisch. N° de réf. du vendeur 9781441931603
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The Nature of Statistical Learning Theory (Information Science and Statistics)
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Nature of Statistical Learning Theory
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Springer, 2010
ISBN 10 : 1441931600
ISBN 13 : 9781441931603
Ancien ou d'occasion
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