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Ajouter au panierEtat : Sehr gut. Zustand: Sehr gut | Seiten: 203 | Sprache: Englisch | Produktart: Bücher.
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Ajouter au panierSoftcover. Second Edition. Octavo, 314 pages. In Good plus condition. Yellow spine with white and brown text. Covers have bumping and light tearing to tail edge of spine, creasing to tail edge of spine, and mild edge wear. Textblock clean. Shelved ND-E. 1376745. FP New Rockville Stock.
Edité par Springer-Verlag New York Inc., 2000
ISBN 10 : 0387945598 ISBN 13 : 9780387945590
Langue: anglais
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Ajouter au panierHardcover. Etat : 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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Ajouter au panierHardcover. Etat : Good. 2nd. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.
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Ajouter au panierEtat : 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.
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Ajouter au panierHardcover. Etat : new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.
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Ajouter au panierhardcover. Etat : New. In shrink wrap. Looks like an interesting title!
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Ajouter au panierHRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.
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Ajouter au panierHRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.
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Ajouter au panierEtat : New.
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Ajouter au panierEtat : New. In English.
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Ajouter au panierEtat : As New. Unread book in perfect condition.
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Ajouter au panierhardcover. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Edité par Springer New York, Springer US, 2010
ISBN 10 : 1441931600 ISBN 13 : 9781441931603
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 249,24
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Ajouter au panierTaschenbuch. 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.
Vendeur : California Books, Miami, FL, Etats-Unis
EUR 266,72
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Ajouter au panierEtat : New.
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
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Ajouter au panierEtat : New.
Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
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Ajouter au panierPaperback. Etat : Like New. Like New. book.
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Ajouter au panierEtat : New.
Vendeur : moluna, Greven, Allemagne
EUR 208,08
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Ajouter au panierEtat : 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.
Edité par Springer New York Dez 2010, 2010
ISBN 10 : 1441931600 ISBN 13 : 9781441931603
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 235,39
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Ajouter au panierTaschenbuch. 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.
Edité par Springer New York, Springer US Dez 2010, 2010
ISBN 10 : 1441931600 ISBN 13 : 9781441931603
Langue: anglais
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 246,09
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Ajouter au panierTaschenbuch. 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.
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 316,17
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Ajouter au panierHardcover. Etat : Brand New. 2nd sub edition. 214 pages. 9.25x6.25x1.00 inches. In Stock. This item is printed on demand.