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The generalization error bounds for the entire input space found by current error models using the number of effective parameters of a classifier and the number of training samples are usually very loose. But classifiers such as SVM, RBFNN and MLPNN, are really local learning machines used for many application problems, which consider unseen samples close to the training samples more important. In...
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