Support Vector Machines for Pattern Classification

Shigeo Abe

ISBN 10: 1447125487 / ISBN 13: 9781447125488

Edité par Springer London, Springer London, 2012

A propos de cet article

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.

N° de réf. du vendeur 9781447125488

Détails bibliographiques

• Titre: Support Vector Machines for Pattern Classification
• Éditeur: Springer London, Springer London
• Date de parution: 2012
• Langue: anglais
• ISBN à 10 chiffres: 1447125487
• ISBN à 13 chiffres: 9781447125488
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

Synopsis

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.

Présentation de l'éditeur

This guide on the use of SVMs in pattern classification includes a rigorous performance comparison of classifiers and regressors. The book takes the unique approach of focusing on classification rather than covering the theoretical aspects of SVMs.