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. Get and download textbook Support Vector Machines for Pattern Classification (Advances in Computer Vision and Pattern Recognition) for free
author shigeo abe format hardback language english publication year 25 08 2005 series advances in computer vision and pattern recognition subject computing it subject 2 computing professional programming title support vector machines for pattern classification advances in pattern recognition author shigeo abe publisher springer verlag new york inc publication date aug 30 2005 pages 343 binding hardcover edition 1 st dimensions 6 25 wx 9 50 hx 0 75 d isbn 1852339292 subject computers artificial
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 new edition
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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 25 08 2005 series advances in computer vision and pattern recognition subject computing it subject 2 computing professional programming title support vector machines for pattern classification advances in pattern recognition author shigeo abe publisher springer verlag new york inc publication date aug 30 2005 pages 343 binding hardcover edition 1 st dimensions 6 25 wx 9 50 hx 0 75 d isbn 1852339292 subject computers artificial
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition), ISBN-13: 9781849960977, ISBN-10: 1849960976
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
Support Vector Machines for Pattern Classification Textbook
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,
