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Publicado por Springer, 2010
ISBN 10: 1441931600ISBN 13: 9781441931603
Librería: booksXpress, Bayonne, NJ, Estados Unidos de America
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Soft Cover. Condición: new.
Publicado por Springer, 2010
ISBN 10: 1441931600ISBN 13: 9781441931603
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Libro Impresión bajo demanda
Condición: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Publicado por Springer, 2010
ISBN 10: 1441931600ISBN 13: 9781441931603
Librería: Books Unplugged, Amherst, NY, Estados Unidos de America
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Condición: New. Buy with confidence! Book is in new, never-used condition.
Publicado por Springer New York, 2010
ISBN 10: 1441931600ISBN 13: 9781441931603
Librería: moluna, Greven, Alemania
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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.
Publicado por Springer, 2010
ISBN 10: 1441931600ISBN 13: 9781441931603
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
Libro
Condición: New.
Publicado por Springer New York Dez 2010, 2010
ISBN 10: 1441931600ISBN 13: 9781441931603
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Libro Impresión bajo demanda
Taschenbuch. Condición: 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.
Publicado por Springer New York, 2010
ISBN 10: 1441931600ISBN 13: 9781441931603
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Libro
Taschenbuch. 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.
Publicado por Springer, 2010
ISBN 10: 1441931600ISBN 13: 9781441931603
Librería: Books Puddle, New York, NY, Estados Unidos de America
Libro
Condición: New. pp. 336 2nd Edition.
Publicado por Springer, 2010
ISBN 10: 1441931600ISBN 13: 9781441931603
Librería: California Books, Miami, FL, Estados Unidos de America
Libro
Condición: New.
Publicado por Springer, 2010
ISBN 10: 1441931600ISBN 13: 9781441931603
Librería: Mispah books, Redhill, SURRE, Reino Unido
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Paperback. Condición: Like New. Like New. book.
Publicado por Springer, 2010
ISBN 10: 1441931600ISBN 13: 9781441931603
Librería: Majestic Books, Hounslow, Reino Unido
Libro Impresión bajo demanda
Condición: New. Print on Demand pp. 336 48 Illus.
Publicado por Springer, 2010
ISBN 10: 1441931600ISBN 13: 9781441931603
Librería: Grumpys Fine Books, Tijeras, NM, Estados Unidos de America
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Paperback. Condición: new. Prompt service guaranteed.
Publicado por Springer, 2010
ISBN 10: 1441931600ISBN 13: 9781441931603
Librería: GoldBooks, Denver, CO, Estados Unidos de America
Libro
Paperback. Condición: new. New Copy. Customer Service Guaranteed.