Support vector machines pattern von abe shigeo (38 Ergebnisse)

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Hardcover. Zustand: Fine. No Jacket. 1st Edition. 2006.Hardcover.Fine.343 pages.Ships from Japan.Usually ships in 1-2 working days.

Zustand: Used. pp. xiv + 343 1st Edition.

Zustand: Used. pp. xiv + 343.

Hardcover. Zustand: New. In shrink wrap. Looks like an interesting title.

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Zustand: New. In English.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - A guide on the use of SVMs in pattern classification, including a rigorous performance comparison of classifiers and regressors. The book presents architectures for multiclass classification and function approximation problems, as well as evaluation criteria for classifiers and regressors. Features: Clarifies the characteristics of two-class SVMs; Discusses kernel methods for improving the generalization ability of neural networks and fuzzy systems; Contains ample illustrations and examples; Includes performance evaluation using publicly available data sets; Examines Mahalanobis kernels, empirical feature space, and the effect of model selection by cross-validation; Covers sparse SVMs, learning using privileged information, semi-supervised learning, multiple classifier systems, and multiple kernel learning; Explores incremental training based batch training and active-set training methods, and decomposition techniques for linear programming SVMs; Discusses variable selection for support vector regressors. …

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - A guide on the use of SVMs in pattern classification, including a rigorous performance comparison of classifiers and regressors. The book presents architectures for multiclass classification and function approximation problems, as well as evaluation criteria for classifiers and regressors. Features: Clarifies the characteristics of two-class SVMs; Discusses kernel methods for improving the generalization ability of neural networks and fuzzy systems; Contains ample illustrations and examples; Includes performance evaluation using publicly available data sets; Examines Mahalanobis kernels, empirical feature space, and the effect of model selection by cross-validation; Covers sparse SVMs, learning using privileged information, semi-supervised learning, multiple classifier systems, and multiple kernel learning; Explores incremental training based batch training and active-set training methods, and decomposition techniques for linear programming SVMs; Discusses variable selection for support vector regressors.…

Hardback. Zustand: New. 2nd ed. 2010. Support Vector Machines are popular because of their high classification importance; this is the first book to focus the discussions on SVMs specifically to pattern classification.

Taschenbuch. Zustand: Neu. Support Vector Machines for Pattern Classification | Shigeo Abe | Taschenbuch | Advances in Computer Vision and Pattern Recognition | xx | Englisch | 2012 | Springer | EAN 9781447125488 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Zustand: New. pp. 492 2nd Edition.

Zustand: New. In English.

Zustand: New. pp. 492 2nd Edition.

Paperback. Zustand: Brand New. 2nd edition. 493 pages. 9.25x6.10x1.18 inches. In Stock.

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Hardback. Zustand: New. 2nd ed. 2010. Support Vector Machines are popular because of their high classification importance; this is the first book to focus the discussions on SVMs specifically to pattern classification.

Paperback. Zustand: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

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Zustand: Used. pp. xiv + 343 Illus. This item is printed on demand.

Zustand: new. Questo è un articolo print on demand.

Zustand: new. Questo è un articolo print on demand.

Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -A guide on the use of SVMs in pattern classification, including a rigorous performance comparison of classifiers and regressors. The book presents architectures for multiclass classification and function approximation problems, as well as evaluation criteria for classifiers and regressors. Features: Clarifies the characteristics of two-class SVMs; Discusses kernel methods for improving the generalization ability of neural networks and fuzzy systems; Contains ample illustrations and examples; Includes performance evaluation using publicly available data sets; Examines Mahalanobis kernels, empirical feature space, and the effect of model selection by cross-validation; Covers sparse SVMs, learning using privileged information, semi-supervised learning, multiple classifier systems, and multiple kernel learning; Explores incremental training based batch training and active-set training methods, and decomposition techniques for linear programming SVMs; Discusses variable selection for support vector regressors. 473 pp. Englisch.…