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Second-Order Cone Programming Formulations for Robust Multiclass Classification

pingsunshine{at}yahoo.com.cn College of Science, China Agricultural University, Beijing, 100083, China

Masao Fukushima

fuku{at}amp.i.kyoto-u.ac.jp Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan

Multiclass classification is an important and ongoing research subject in machine learning. Current support vector methods for multiclass classification implicitly assume that the parameters in the optimization problems are known exactly. However, in practice, the parameters have perturbations since they are estimated from the training data, which are usually subject to measurement noise. In this article, we propose linear and nonlinear robust formulations for multiclass classification based on the M-SVM method. The preliminary numerical experiments confirm the robustness of the proposed method.