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When learning a support vector machine (SVM) from a set of labeled development patterns, the ultimate goal is to get a classifier attaining a low error rate on new patterns. This so-called generalization ability obviously depends on the choices of the learning parameters that control the learning process. Model selection is the method for identifying appropriate values for these parameters. In this...
Evaluating the generalization performance of learning machines without using additional test samples is one of the most important issues in the machine learning community. The subspace information criterion (SIC) is one of the methods for this purpose, which is shown to be an unbiased estimator of the generalization error with finite samples. In this paper, we give ε-insensitive modification of the...
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