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Sprache: Englisch
Verlag: World Scientific Pub Co Inc (edition ), 2002
ISBN 10: 9812381511 ISBN 13: 9789812381514
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Sprache: Englisch
Verlag: World Scientific Pub Co Inc, 2002
ISBN 10: 9812381511 ISBN 13: 9789812381514
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hardcover. Zustand: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Sprache: Englisch
Verlag: World Scientific Pub Co Inc, 2002
ISBN 10: 9812381511 ISBN 13: 9789812381514
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hardcover. Zustand: Good. No dust jacket. Ex-library book with typical stickers and stampings. Priority Mail is available on this item. No international shipping.
Sprache: Englisch
Verlag: World Scientific Pub Co Inc, 2002
ISBN 10: 9812381511 ISBN 13: 9789812381514
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hardcover. Zustand: Very Good. Yellowed dust jacket. Priority Mail is available on this item. No international shipping.
Sprache: Englisch
Verlag: World Scientific Pub Co Inc, 2002
ISBN 10: 9812381511 ISBN 13: 9789812381514
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Sprache: Englisch
Verlag: World Scientific Pub Co Inc, 2002
ISBN 10: 9812381511 ISBN 13: 9789812381514
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Sprache: Englisch
Verlag: World Scientific Publishing Co Pte Ltd, 2002
ISBN 10: 9812381511 ISBN 13: 9789812381514
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In den WarenkorbHRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Sprache: Englisch
Verlag: World Scientific Publishing Co Pte Ltd, SG, 2002
ISBN 10: 9812381511 ISBN 13: 9789812381514
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Hardback. Zustand: New. This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics.The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.
Sprache: Englisch
Verlag: World Scientific Pub Co Inc, 2002
ISBN 10: 9812381511 ISBN 13: 9789812381514
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In den WarenkorbHardback. Zustand: New. This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics.The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.
Sprache: Englisch
Verlag: World Scientific Pub Co Inc, 2002
ISBN 10: 9812381511 ISBN 13: 9789812381514
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Sprache: Englisch
Verlag: Society for Industrial and Applied Mathematics (SIAM), 2006
ISBN 10: 0898716039 ISBN 13: 9780898716030
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Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
Sprache: Englisch
Verlag: World Scientific Pub Co Inc, 2003
ISBN 10: 9812381511 ISBN 13: 9789812381514
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In den WarenkorbHardcover. Zustand: Brand New. 308 pages. 9.25x6.25x1.00 inches. In Stock.
Sprache: Englisch
Verlag: World Scientific Publishing Co Pte Ltd, SG, 2002
ISBN 10: 9812381511 ISBN 13: 9789812381514
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In den WarenkorbHardback. Zustand: New. This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics.The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.
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Sprache: Englisch
Verlag: World Scientific Publishing Company, 2002
ISBN 10: 9812381511 ISBN 13: 9789812381514
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hardcover. Zustand: New. In shrink wrap. Looks like an interesting title!
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Sprache: Englisch
Verlag: Springer Berlin Heidelberg, 2013
ISBN 10: 3642267513 ISBN 13: 9783642267512
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Verlag: Springer Berlin Heidelberg, 2011
ISBN 10: 3642194052 ISBN 13: 9783642194054
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Sprache: Englisch
Verlag: World Scientific Publishing Co Pte Ltd, SG, 2002
ISBN 10: 9812381511 ISBN 13: 9789812381514
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In den WarenkorbHardback. Zustand: New. This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics.The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.
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Sprache: Englisch
Verlag: Springer-Verlag New York Inc, 2013
ISBN 10: 3642267513 ISBN 13: 9783642267512
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In den WarenkorbPaperback. Zustand: Brand New. 2011 edition. 228 pages. 9.25x6.10x0.54 inches. In Stock.