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Support Vector Machines Pattern by Abe Shigeo (35 results)
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Hardcover. Condition: Fine. Leichte Risse. 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.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Support Vector Machines for Pattern Classification (Advances in Computer Vision and Pattern Recognition)
Seller: Corner of a Foreign Field, Tokyo, TOKYO, Japan
Seller rating 5 out of 5 stars
First Edition
Hardcover. Condition: Fine. No Jacket. 1st Edition. 2006.Hardcover.Fine.343 pages.Ships from Japan.Usually ships in 1-2 working days.
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Brand new book. Fast ship. Please provide full street address as we are not able to ship to P O box address.
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Condition: New. pp. xiv + 343 1st Edition.
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Condition: New. pp. xiv + 343.
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Hardcover. Condition: New. In shrink wrap. Looks like an interesting title!
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Hardcover. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Support Vector Machines for Pattern Classification (Advances in Computer Vision and Pattern Recognition)
Book 13 of 86: Advances in Computer Vision and Pattern RecognitionSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 181.78
US$ 15.76 shipping from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Support Vector Machines for Pattern Classification
Book 13 of 86: Advances in Computer Vision and Pattern RecognitionSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 181.77
US$ 19.74 shipping from United Kingdom to U.S.A.Quantity: Over 20 available
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Support Vector Machines for Pattern Classification (Advances in Computer Vision and Pattern Recognition)
Book 13 of 86: Advances in Computer Vision and Pattern RecognitionSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 222.44
US$ 15.76 shipping from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Condition: New. pp. 492 2nd Edition.
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Condition: New. pp. 492 2nd Edition.
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Support Vector Machines for Pattern Classification
Book 13 of 86: Advances in Computer Vision and Pattern RecognitionPublished by Springer London Ltd, GB, 2010
ISBN 10: 1849960976 ISBN 13: 9781849960977
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. 2nd ed. 2010. 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.
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Support Vector Machines for Pattern Classification
Book 13 of 86: Advances in Computer Vision and Pattern RecognitionPublished by Springer London, Springer London, 2012
ISBN 10: 1447125487 ISBN 13: 9781447125488
Language: English
Taschenbuch. Condition: 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.
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Buch. Condition: 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.
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Support Vector Machines for Pattern Classification
Book 13 of 86: Advances in Computer Vision and Pattern RecognitionSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 269.69
US$ 19.74 shipping from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
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Support Vector Machines for Pattern Classification
Book 13 of 86: Advances in Computer Vision and Pattern RecognitionPublished by Springer-Verlag New York Inc, 2012
ISBN 10: 1447125487 ISBN 13: 9781447125488
Language: English
Paperback. Condition: Brand New. 2nd edition. 493 pages. 9.25x6.10x1.18 inches. In Stock.
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Paperback. Condition: Like New. Like New. book.
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Hardcover. Condition: Like New. Like New. book.
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Condition: As New. Unread book in perfect condition.
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Support Vector Machines for Pattern Classification
Book 13 of 86: Advances in Computer Vision and Pattern RecognitionPublished by Springer London Ltd, GB, 2010
ISBN 10: 1849960976 ISBN 13: 9781849960977
Language: English
US$ 250.69
US$ 85.53 shipping from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketHardback. Condition: New. 2nd ed. 2010. 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.
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Condition: New. pp. xiv + 343 Illus. This item is printed on demand.
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Support Vector Machines for Pattern Classification
Book 13 of 86: Advances in Computer Vision and Pattern RecognitionPublished by SPRINGER NATURE Mrz 2010, 2010
ISBN 10: 1849960976 ISBN 13: 9781849960977
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: 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.
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Support Vector Machines for Pattern Classification
Book 13 of 86: Advances in Computer Vision and Pattern RecognitionPublished by Springer London Mai 2012, 2012
ISBN 10: 1447125487 ISBN 13: 9781447125488
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. 492 pp. Englisch.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. A comprehensive resource for the use of Support Vector Machines in Pattern ClassificationTakes the unique approach of focussing on classification rather than covering the theoretical aspects of Support Vector MachinesIncludes application of.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. A comprehensive resource for the use of Support Vector Machines in Pattern ClassificationTakes the unique approach of focussing on classification rather than covering the theoretical aspects of Support Vector MachinesIncludes application of.
