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An optimizing kernel algorithm for improving the performance of support vector domain description. (Chinese. English summary) Zbl 1174.68530

Summary: The support vector domain description is a robust data domain description method. Its performance, however, is strongly influenced by kernel parameter. In this paper, we present a novel parameter-optimizing algorithm based on the idea that the optimal parameter can lead to a hypersphere-shaped distribution of the mapped data in the feature space. Firstly, based on an orthogonal basis of the subspace spanned by the mapped data, a way is given to capture the structure of the entire mapped data, which avoids the problem that the mapped data cannot be expressed in an explicit form. Secondly, based on the maximum-entropy non-Gaussian measurement, a new criterion is presented for estimating the degree for a distribution to be closed to the hypersphere area and it is used to select the suitable kernel parameter. The experiments on simulated data and real-world data demonstrate the effectiveness of the proposed method.

MSC:

68T05 Learning and adaptive systems in artificial intelligence
68T10 Pattern recognition, speech recognition
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