9780387987804 - the nature of statistical learning theory di vapnik, vladimir naumovich (24 risultati)
Lingua: Inglese
Editore: Springer, 1999
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Hardcover. Condizione: Gut. 2. Auflage. ZUSTAND GUT.
Lingua: Inglese
Editore: Springer, 1999
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hardcover. Condizione: New. In shrink wrap. Looks like an interesting title.
Lingua: Inglese
Editore: Springer, 1999
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Condizione: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.
Lingua: Inglese
Editore: Springer, 1999
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Condizione: New.
Lingua: Inglese
Editore: Springer, 1999
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Condizione: New.
Lingua: Inglese
Editore: Springer, 1999
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HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Lingua: Inglese
Editore: Springer, 1999
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HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Lingua: Inglese
Editore: Springer, 1999
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Condizione: New. In English.
Lingua: Inglese
Editore: Springer, 1999
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Condizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Springer, 1999
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Condizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Springer-Verlag New York Inc., US, 1999
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Hardback. Condizione: 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.
Lingua: Inglese
Editore: Springer US, 1999
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Da: BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, GermaniaBUCHSERVICE / ANTIQUARIAT Lars Lutzer
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Condizione: gut. The Nature of Statistical Learning Theory (Information Science and Statistics) In englischer Sprache. pages.
Lingua: Inglese
Editore: Springer New York, 1999
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Da: moluna, Greven, Germaniamoluna
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Condizione: 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.
Lingua: Inglese
Editore: Springer-Verlag Gmbh Dez 2000, 2000
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Da: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, GermaniaRheinberg-Buch Andreas Meier eK
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Buch. Condizione: Neu. 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 t…he 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 314 pp. Englisch.
Lingua: Inglese
Editore: Springer-Verlag Gmbh Dez 2000, 2000
- Rilegato
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condizione: Neu. 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 t…he 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 314 pp. Englisch.
Lingua: Inglese
Editore: Springer-Verlag Gmbh Dez 2000, 2000
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Da: Wegmann1855, Zwiesel, GermaniaWegmann1855
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Buch. Condizione: Neu. 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 t…he main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
Lingua: Inglese
Editore: Springer, 1999
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condizione: New. pp. 340 2nd Edition.
Altre immaginiLingua: Inglese
Editore: Springer-Verlag GmbH, 1999
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Da: preigu, Osnabrück, Germaniapreigu
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Buch. Condizione: Neu. The Nature of Statistical Learning Theory | V. N. Vapnik | Buch | Information Science and Statistics | xx | Englisch | 1999 | Springer-Verlag GmbH | EAN 9780387987804 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | A…nbieter: preigu.
Lingua: Inglese
Editore: Springer, 1999
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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EUR 312,45
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Condizione: New. pp. 340 Illus.
Lingua: Inglese
Editore: Springer-Verlag Gmbh Dez 2000, 2000
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Buch. Condizione: Neu. 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 t…he main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 314 pp. Englisch.
Lingua: Inglese
Editore: Springer-Verlag New York Inc., US, 1999
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Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
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EUR 261,23
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Hardback. Condizione: 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.
Lingua: Inglese
Editore: Springer-Verlag Gmbh Dez 2000, 2000
- Rilegato
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 267,49
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Buch. Condizione: Neu. 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.
Lingua: Inglese
Editore: Springer-Verlag Gmbh Dez 2000, 2000
- Rilegato
Da: Books-by-Floh, Paderborn, GermaniaBooks-by-Floh
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Buch. Condizione: Neu. Neuware -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, mathematicians, physicists, and computer scientists.…314 pp. Englisch.
Lingua: Inglese
Editore: Springer Verlag, 1999
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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EUR 343,61
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Hardcover. Condizione: Brand New. 2nd sub edition. 214 pages. 9.25x6.25x1.00 inches. In Stock. This item is printed on demand.
