Abstract
Support Vector Machines (SVMs) for classification – in short SVM – have been shown to be promising classification tools in many real-world problems. How to effectively extend binary SVC to multi-class classification is still an on-going research issue. In this article, instead of solving quadratic programming (QP) in Algorithm K-SVCR and Algorithm ν-K-SVCR, a linear programming (LP) problem is introduced in our algorithm. This leads to a new algorithm for multi-class problem, K-class Linear programming ν–Support Vector Classification-Regression(Algorithm ν-K-LSVCR). Numerical experiments on artificial data sets and benchmark data sets show that the proposed method is comparable to Algorithm K-SVCR and Algorithm ν-K-SVCR in errors, while considerably faster than them.
This work is supported by the National Natural Science Foundation of China (No.10371131).
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Qi, Z., Tian, Y., Deng, N. (2005). A New Support Vector Machine for Multi-class Classification. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_85
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DOI: https://doi.org/10.1007/11596448_85
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