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Hyperparameter design criteria for support vector classifiers
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DOI：10.1016/S0925-2312(03)00430-2.png)
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Abstract
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The design of a support vector machine (SVM) consists in tuning a set of hyperparameter quantities, and requires an accurate prediction of the classifier's generalization performance. The paper describes the application of the maximal-discrepancy criterion to the hyperparameter-setting process, and points out the advantages of such an approach over existing theoretical frameworks. The resulting theoretical predictions are then compared with the k-fold cross-validation empirical method, which probably is the current best-performing approach to the SVM design problem. Experimental results on a wide range of real-world testbeds prove out that the features of the maximal-discrepancy method can notably narrow the gap that so far has separated theoretical and empirical estimates of a classifier's generalization error. (C) 2003 Elsevier B.V. All rights reserved.
Keywords:
generalization error estimate
hyperparameters tuning
k-fold cross-validation
maximal-discrepancy criterion
support vector classifier
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6.5
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2.5W
Citations:
6.5W
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