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In den WarenkorbZustand: New. pp. 768.
Anbieter: moluna, Greven, Deutschland
Zustand: 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.
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. The Nature of Statistical Learning Theory | Vladimir Vapnik | Taschenbuch | Information Science and Statistics | xx | Englisch | 2010 | Humana | EAN 9781441931603 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Zustand: gut. The Nature of Statistical Learning Theory (Information Science and Statistics) In deutscher Sprache. pages.
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In den WarenkorbZustand: New. This book is devoted to the statistical theory of learning and generalization, that is, the problem of choosing the desired function on the basis of empirical data. The author will present the whole picture of learning and generalization theory. Learning theory has applications in many fields, such as psychology, education and computer science. Series: Adaptive and Learning Systems for Signal Processing, Communications and Control Series. Num Pages: 768 pages, Illustrations. BIC Classification: PBKF; UYQM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 163 x 235 x 41. Weight in Grams: 1214. . 1998. Hardcover. . . . . Books ship from the US and Ireland.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. 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.
Buch. Zustand: Neu. Neuware - Dieses Buch widmet sich der statistischen Theorie des Lernens und der Verallgemeinerung - das heißt, dem Problem der Auswahl der gewünschten Funktion auf der Basis empirischen Datenmaterials. Anwendung findet die Theorie auf vielen verschiedenen Gebieten - in neuronalen Netzwerken, Fuzzy-Logic-Systemen und künstlicher Intelligenz - beispielsweise in der Psychologie und der Informationswissenschaft. (8/98).
Verlag: Springer Verlag, New York, 1982
Anbieter: The Chatham Bookseller, Madison, NJ, USA
Erstausgabe
Hardcover. Zustand: Very Good. Zustand des Schutzumschlags: No Jacket as Issued. 1st Edition. 399pp.Yellow boards. 22 illustrations Marks on cover and back, pages clean and hinges tight."Estimation of Dependences Based on Empirical Data" is the title of a foundational book and related papers, primarily by Vladimir Vapnik, that describes how to build statistical models from data to understand relationships and make predictions. Size: octavo. Book.