Free Support Vector Machines for Pattern Classification

Author: Shigeo Abe
Edition: 2nd ed. 2010
Binding: Hardcover
ISBN: 1849960976
Publisher: Springer
Features:

Support Vector Machines for Pattern Classification (Advances in Computer Vision and Pattern Recognition)

A guide on the use of SVMs in pattern classification, including a rigorous performance comparison of classifiers and regressors. Search and download computer ebooks Support Vector Machines for Pattern Classification (Advances in Computer Vision and Pattern Recognition) for free.
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition), ISBN-13: 9781849960977, ISBN-10: 1849960976. Download Support Vector Machines for Pattern Classification computer ebooks
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,

Support Vector Machines for Pattern Classification

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, empir

author shigeo abe format hardback language english publication year 29 03 2010 series advances in computer vision and pattern recognition subject engineering technology subject 2 electronics engineering communications engineering support vector machines for pattern classification advances in computer vision author s shigeo abe content note 1 black white illustrations country of publication united kingdom date of publication 29 03 2010 edition statement 2 nd ed 2010 format hardback format details

Support Vector Machines for Pattern Classification provides a comprehensive resource for the use of SVM s in pattern classification The subject area is particularly timely with research on kernel methods increasing rapidly this book is unique in its focus on classification methods The characteristic SVM s are discussed L1 SVMs and L2 SVMs lease squares SVMs and linear programming SVMs from both a theoretical and an experimental viewpoint SVMs were originally formulated for two class problems and an extension to multiclass systems which are essential for practical use is not unique However in i

Store Search search Title, ISBN and Author Support Vector Machines for Pattern Classification by Shigeo Abe Estimated delivery 3-12 business days Format Paperback Condition Brand New 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 c

Support Vector Machines for Pattern Classification Free

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,