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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