Maggiori informazioni sul libro
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.
Acquisto del libro
Support Vector Machines for Pattern Classification, Shigeo Abe
- Lingua
- Pubblicato
- 2010
- product-detail.submit-box.info.binding
- (Copertina rigida)
Metodi di pagamento
Qui potrebbe esserci la tua recensione.
- Titolo
- Support Vector Machines for Pattern Classification
- Lingua
- Inglese
- Autori
- Shigeo Abe
- Editore
- Springer London
- Pubblicato
- 2010
- Formato
- Copertina rigida
- Pagine
- 471
- ISBN10
- 1849960976
- ISBN13
- 9781849960977
- Serie
- Valutazione
- 4,2 su 5
- Descrizione
- 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.