Efficient performance estimate for one-class support vector machine

doi:10.1016/j.patrec.2004.11.001

Efficient performance estimate for one-class support vector machine

2005-06-01

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DOI：10.1016/j.patrec.2004.11.001

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This letter proposes and analyzes a method (xi alpha p-estimate) to estimate the generalization performance of one-class support vector machine (SVM) for novelty detection. The method is an extended version of the a-estimate method, which is used to estimate the generalization performance of standard SVM for classification. Our method is derived from analyzing the connection between one-class SVM and standard SVM. Without any computation intensive re-sampling, the method is computationally much more efficient than leave-one-out method, since it can be computed immediately from the decision function of one-class SVM. Using our method to estimate the error rate is more precise than using the fraction of support vectors and a parameter v of one-class SVM. We also propose that the fraction of support vectors characterizes the precision of one-class SVM. A theoretical analysis and experiments on an artificial data and a widely known handwritten digit recognition set (MNIST) show that our method can effectively estimate the generalization performance of one-class SVM for novelty detection. (c) 2004 Elsevier B.V. All rights reserved.

Keyword:

performance estimate

one-class support vector machines

support vector machines

novelty detection

Efficient performance estimate for one-class support vector machine

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