Support Vector Machines for Pattern Classification (Advances in Computer Vision and Pattern Recognition) 2nd Edition by Shigeo Abe (PDF)

Description

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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⭐I was impressed by how fast this book was delivered. It came in neat, and better than described on the site. I don’t normally do reviews, but I am forced to do this one.Besides, you can’t beat their price elsewhere I tell you!

⭐Excellent resource, full of useful equations and information.

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Support Vector Machines for Pattern Classification (Advances in Computer Vision and Pattern Recognition) 2nd Edition PDF Free Download
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Support Vector Machines for Pattern Classification (Advances in Computer Vision and Pattern Recognition) 2nd Edition 2010 PDF Free Download
Download Support Vector Machines for Pattern Classification (Advances in Computer Vision and Pattern Recognition) 2nd Edition PDF
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