Abstract
Support vector machine (SVM) is a very promising classification technique developed by Vapnik. However, there are still some shortcomings in the original SVM approach. First, SVM was originally designed for binary classification. How to extend it effectively for multi-class classification is still an on-going research issue. Second, SVM does not consider the distribution of each class. In this paper, we propose an extension to the SVM method of pattern recognition for solving the multi-class problem in one formal step. Contrast to previous multi-class SVMs, our approach considers the distribution of each class. Experimental results show that the proposed method is more suitable for practical use than other multi-class SVMs, especially for unbalanced datasets.
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Hao, PY., Lin, YH. (2007). A New Multi-class Support Vector Machine with Multi-sphere in the Feature Space. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_75
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DOI: https://doi.org/10.1007/978-3-540-73325-6_75
Publisher Name: Springer, Berlin, Heidelberg
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