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Support Vector Machines for Pattern Classification
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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, 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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Subject categories
- Academic > Mathematics > General > Mathematics
- Academic > Computer Science > Computer science
- Academic > Computer Science > Electronic data processing
- Academic > Computer Science > Computers - special aspects
- Academic > Computer Science > System design; Periodicals
- Academic > Computer Science > Text processing
- Academic > Mathematics > Instruments and machines
- Academic > Mathematics > Geometry. Trigonometry.Topology
- Computers > Computer Vision
- Computers > Information Technology
- Technology > Electronics
ISBNs
1849960984
9781849960977
9781849960984