Abstract
Support Vector Machines (SVMs) are efficient tools, which have been widely studied and used in many fields. However, original SVM (C-SVM) only focuses on the scatter between classes, but neglects the global information about the data which are also vital for an optimal classifier. Therefore, C-SVM loses some robustness. To solve this problem, one approach is to translate (i.e., to move without rotation or change of shape) the hyperplane according to the global characteristics of the data. However, parts of existing work using this approach are based on specific distribution assumption (S-SVM), while the rest fail to utilize the global information (GS-SVM). In this paper, we propose a simple but efficient method based on weighted scatter degree (WSD-SVM) to embed the global information into GS-SVM without any distribution assumptions. A comparison of WSD-SVM, C-SVM and GS-SVM is conducted, and the results on several data sets show the advantages of WSD-SVM.
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Jin, AL., Zhou, X., Ye, CZ. (2011). Support Vector Machines Based on Weighted Scatter Degree. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_77
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DOI: https://doi.org/10.1007/978-3-642-23896-3_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23895-6
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