Support Vector Machines (Information Science and Statistics)
Steinwart, Ingo; Christmann, Andreas
ISBN 10: 0387772413 ISBN 13: 9780387772417
Edité par Springer, 2008
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Description :
In. N° de réf. du vendeur ria9780387772417_new
Synopsis :
This book provides a deep but still self-contained introduction to Support Vector Machines (SVMs) and their role in statistical problems.
À propos de l?auteur:
Ingo Steinwart is a researcher in the machine learning group at the Los Alamos National Laboratory. He works on support vector machines and related methods.
Andreas Christmann is Professor of Stochastics in the Department of Mathematics at the University of Bayreuth. He works in particular on support vector machines and robust statistics.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Détails bibliographiques
Titre : Support Vector Machines (Information Science...
Éditeur : Springer
Date d'édition : 2008
Reliure : Couverture rigide
Etat : New
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Support Vector Machines
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ISBN 10 : 0387772413
ISBN 13 : 9780387772417
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Explains the principles that make support vector machines a successful modelling and prediction tool for a variety of applicationsRigorous treatment of state-of-the-art results on support vector machinesSuitable for both graduate students a. N° de réf. du vendeur 5911114
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Support Vector Machines
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Buch. Etat : Neu. Support Vector Machines | Andreas Christmann (u. a.) | Buch | xvi | Englisch | 2008 | Springer | EAN 9780387772417 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. N° de réf. du vendeur 101847892
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Support Vector Machines
Edité par
Springer New York, Springer New York Aug 2008, 2008
ISBN 10 : 0387772413
ISBN 13 : 9780387772417
Neuf
Buch
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Buch. Etat : Neu. Neuware -Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try to achieve this by presenting the basic ideas of SVMs together with the latest developments and current research questions in a uni ed style. In a nutshell, we identify at least three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and last but not least their computational e ciency compared with several other methods. Although there are several roots and precursors of SVMs, these methods gained particular momentum during the last 15 years since Vapnik (1995, 1998) published his well-known textbooks on statistical learning theory with aspecialemphasisonsupportvectormachines. Sincethen,the eldofmachine learninghaswitnessedintenseactivityinthestudyofSVMs,whichhasspread moreandmoretootherdisciplinessuchasstatisticsandmathematics. Thusit seems fair to say that several communities are currently working on support vector machines and on related kernel-based methods. Although there are many interactions between these communities, we think that there is still roomforadditionalfruitfulinteractionandwouldbegladifthistextbookwere found helpful in stimulating further research. Many of the results presented in this book have previously been scattered in the journal literature or are still under review. As a consequence, these results have been accessible only to a relativelysmallnumberofspecialists,sometimesprobablyonlytopeoplefrom one community but not the others.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 620 pp. Englisch. N° de réf. du vendeur 9780387772417
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Support Vector Machines
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try to achieve this by presenting the basic ideas of SVMs together with the latest developments and current research questions in a uni ed style. In a nutshell, we identify at least three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and last but not least their computational e ciency compared with several other methods. Although there are several roots and precursors of SVMs, these methods gained particular momentum during the last 15 years since Vapnik (1995, 1998) published his well-known textbooks on statistical learning theory with aspecialemphasisonsupportvectormachines. Sincethen,the eldofmachine learninghaswitnessedintenseactivityinthestudyofSVMs,whichhasspread moreandmoretootherdisciplinessuchasstatisticsandmathematics. Thusit seems fair to say that several communities are currently working on support vector machines and on related kernel-based methods. Although there are many interactions between these communities, we think that there is still roomforadditionalfruitfulinteractionandwouldbegladifthistextbookwere found helpful in stimulating further research. Many of the results presented in this book have previously been scattered in the journal literature or are still under review. As a consequence, these results have been accessible only to a relativelysmallnumberofspecialists,sometimesprobablyonlytopeoplefrom one community but not the others. 620 pp. Englisch. N° de réf. du vendeur 9780387772417
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Support Vector Machines
Edité par
Springer New York, Springer New York, 2008
ISBN 10 : 0387772413
ISBN 13 : 9780387772417
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try to achieve this by presenting the basic ideas of SVMs together with the latest developments and current research questions in a uni ed style. In a nutshell, we identify at least three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and last but not least their computational e ciency compared with several other methods. Although there are several roots and precursors of SVMs, these methods gained particular momentum during the last 15 years since Vapnik (1995, 1998) published his well-known textbooks on statistical learning theory with aspecialemphasisonsupportvectormachines. Sincethen,the eldofmachine learninghaswitnessedintenseactivityinthestudyofSVMs,whichhasspread moreandmoretootherdisciplinessuchasstatisticsandmathematics. Thusit seems fair to say that several communities are currently working on support vector machines and on related kernel-based methods. Although there are many interactions between these communities, we think that there is still roomforadditionalfruitfulinteractionandwouldbegladifthistextbookwere found helpful in stimulating further research. Many of the results presented in this book have previously been scattered in the journal literature or are still under review. As a consequence, these results have been accessible only to a relativelysmallnumberofspecialists,sometimesprobablyonlytopeoplefrom one community but not the others. N° de réf. du vendeur 9780387772417
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Support Vector Machines
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