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In den WarenkorbSoftcover. 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.
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In den WarenkorbPaperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 1.04.
Verlag: Springer-Verlag New York Inc., 2000
ISBN 10: 0387945598 ISBN 13: 9780387945590
Sprache: Englisch
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Hardcover. Zustand: 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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In den WarenkorbZustand: Sehr gut. Zustand: Sehr gut - Gepflegter, sauberer Zustand. 1. Auflage. Aus der Auflösung einer renommierten Bibliothek. Kann Stempel beinhalten. | Seiten: 203 | Sprache: Englisch | Produktart: Bücher.
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In den WarenkorbZustand: New. In English.
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In den WarenkorbZustand: 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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Verlag: Springer New York, Springer US, 2010
ISBN 10: 1441931600 ISBN 13: 9781441931603
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
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In den WarenkorbTaschenbuch. Zustand: 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.
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In den WarenkorbPaperback. Zustand: Like New. Like New. book.
Verlag: Springer New York Dez 2010, 2010
ISBN 10: 1441931600 ISBN 13: 9781441931603
Sprache: Englisch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
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In den WarenkorbTaschenbuch. Zustand: 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.
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In den WarenkorbZustand: 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.
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In den WarenkorbHardcover. Zustand: Brand New. 2nd sub edition. 214 pages. 9.25x6.25x1.00 inches. In Stock. This item is printed on demand.