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Erschienen in:
2013 | OriginalPaper | Buchkapitel
SVM-SVDD: A New Method to Solve Data Description Problem with Negative Examples
verfasst von : Zhigang Wang, Zeng-Shun Zhao, Changshui Zhang
Erschienen in: Advances in Neural Networks – ISNN 2013
Verlag: Springer Berlin Heidelberg
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Support Vector Data Description(SVDD) is an important method to solve data description or one-class classification problem. In original data description problem, only positive examples are provided in training. The performance of SVDD can be improved when a few negative examples are available which is known as SVDD_neg. Intuitively, these negative examples should cause an improvement on performance than SVDD. However, the performance of SVDD may become worse when some negative examples are available. In this paper, we propose a new approach “SVM-SVDD”, in which Support Vector Machine(SVM) helps SVDD to solve data description problem with negative examples efficiently. SVM-SVDD obtains its solution by solving two convex optimization problems in two steps. We show experimentally that our method outperforms SVDD_neg in both training time and accuracy.