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ISBN 10: 3930038471 ISBN 13: 9783930038473
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Language: German
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Published by Fischer-Nagel, Heiderose, 2015
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ISBN 10: 3930038471 ISBN 13: 9783930038473
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Published by Fischer-Nagel, Heiderose, 2015
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Language: German
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Language: English
Published by Oxford University Press, 2010
ISBN 10: 0199589976 ISBN 13: 9780199589975
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Language: German
ISBN 10: 3209037205 ISBN 13: 9783209037206
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Language: German
Published by Fischer-Nagel, Heiderose, 2015
ISBN 10: 3930038471 ISBN 13: 9783930038473
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Language: English
Published by Oxford University Press, 2010
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Published by Mainz: Hermann Schmidt, 1997
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264 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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Language: German
Published by Peter Lang GmbH, Internationaler Verlag der Wissenschaften, 2009
ISBN 10: 3899757009 ISBN 13: 9783899757002
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ISBN 10: 3899757009 ISBN 13: 9783899757002
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Language: English
Published by Springer New York, Springer New York Aug 2008, 2008
ISBN 10: 0387772413 ISBN 13: 9780387772417
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Buch. Condition: 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.
Language: English
Published by Springer New York, Springer New York, 2008
ISBN 10: 0387772413 ISBN 13: 9780387772417
Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: 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.
Language: German
Published by Peter Lang GmbH, Internationaler Verlag der Wissenschaften, 2009
ISBN 10: 3899757009 ISBN 13: 9783899757002
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Published by Leipzig, 1999, 1999
Seller: Antiquariaat Pieter Judo (De Lezenaar), Hasselt, Belgium
Association Member: ILAB
234 + 4 + 52pp., with 61 bl/w ills., 21cm., softcover, text in German, Doctoral dissertation (Von der Fakultät für Geschichte, Kunst- und Orientwissenschaften der Universität Leipzig angenommene Dissertation zur Erlangung des akademischen Grades Doctor Philosophiae), stamp at verso of title page, text is clean and bright (looks unread), good condition, X110812.
Language: German
Published by Peter Lang GmbH, Internationaler Verlag der Wissenschaften, 2009
ISBN 10: 3899757009 ISBN 13: 9783899757002
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