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Editore: Spangenberg, Fischer-Nagel, 2015
ISBN 10: 3930038471 ISBN 13: 9783930038473
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Editore: Fischer-Nagel, H; 1. Edition (2. Februar 2015), 2015
ISBN 10: 3930038471 ISBN 13: 9783930038473
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Editore: Mainz: Hermann Schmidt, 1997
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Aggiungi al carrello264 S. 4°, OLn. kasch., OU an oberer Kante etw. angestoßen, sonst fast neuwertiger Zustand. Frontispiz: Porträt Prof. Imiela. Sehr schöne typografische Buchgestaltung. 1400 gr.
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Lingua: Tedesco
Editore: Peter Lang GmbH, Internationaler Verlag der Wissenschaften, 2009
ISBN 10: 3899757009 ISBN 13: 9783899757002
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Lingua: Inglese
Editore: Springer New York, Springer New York Aug 2008, 2008
ISBN 10: 0387772413 ISBN 13: 9780387772417
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 246,09
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Aggiungi al carrelloBuch. Condizione: 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.
Lingua: Inglese
Editore: Springer New York, Springer New York, 2008
ISBN 10: 0387772413 ISBN 13: 9780387772417
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 251,15
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Aggiungi al carrelloBuch. Condizione: 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.
Lingua: Tedesco
Editore: Peter Lang GmbH, Internationaler Verlag der Wissenschaften, 2009
ISBN 10: 3899757009 ISBN 13: 9783899757002
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New.