Abstract
Current well-known data description method such as Support Vector Data Description is conducted with assumption that data samples of a class in feature space are drawn from a single distribution. Based on this assumption, a single hypersphere is constructed to provide a good data description for the data. However, real-world data samples may be drawn from some distinctive distributions and hence it does not guarantee that a single hypersphere can offer the best data description. In this paper, we introduce a Deterministic Annealing Multi-sphere Support Vector Data Description (DAMS-SVDD) approach to address this issue. We propose to use a set of hyperspheres to provide a better data description for a given data set. Calculations for determining optimal hyperspheres and experimental results for applying this proposed approach to classification problems are presented.
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© 2012 Springer-Verlag Berlin Heidelberg
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Le, T., Tran, D., Ma, W., Sharma, D. (2012). Deterministic Annealing Multi-Sphere Support Vector Data Description. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_23
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DOI: https://doi.org/10.1007/978-3-642-34487-9_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34486-2
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