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Publicado por Springer-Verlag New York Inc., 2000
ISBN 10: 0387945598 ISBN 13: 9780387945590
Idioma: Inglés
Librería: Ammareal, Morangis, Francia
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Añadir al carritoHardcover. Condición: Bon. Ancien livre de bibliothèque avec équipements. Edition 2000. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Edition 2000. Ammareal gives back up to 15% of this item's net price to charity organizations.
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Publicado por Springer (edition 2nd), 1999
ISBN 10: 0387987800 ISBN 13: 9780387987804
Idioma: Inglés
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Añadir al carritoGebunden. Condición: New. 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.
Publicado por Springer-Verlag New York Inc., US, 1999
ISBN 10: 0387987800 ISBN 13: 9780387987804
Idioma: Inglés
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
EUR 311,74
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Añadir al carritoHardback. Condición: New. Second Edition 2000. 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 ATandT Labs-Research and Professor of London University. He is one of the founders of.
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Añadir al carritoHardcover. Condición: new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.
Publicado por Springer New York, Springer US, 2010
ISBN 10: 1441931600 ISBN 13: 9781441931603
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoTaschenbuch. Condición: 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.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 326,16
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Añadir al carritoCondición: New. pp. 340 2nd Edition.
Publicado por Springer-Verlag New York Inc., US, 1999
ISBN 10: 0387987800 ISBN 13: 9780387987804
Idioma: Inglés
Librería: Rarewaves.com UK, London, Reino Unido
EUR 283,63
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Añadir al carritoHardback. Condición: New. Second Edition 2000. 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 ATandT Labs-Research and Professor of London University. He is one of the founders of.
EUR 345,00
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Librería: Mispah books, Redhill, SURRE, Reino Unido
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Añadir al carritoPaperback. Condición: Like New. Like New. book.
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Librería: moluna, Greven, Alemania
EUR 206,40
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Añadir al carritoKartoniert / Broschiert. Condición: 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.