title: Second-order cone programming formulations for robust multiclass classification.
abstract: 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.
authors.affiliation: College of Science, China Agricultural University, Beijing, 100083, China. pingsunshine@yahoo.com.cn
authors.forename: Ping
authors.initials: P
authors.lastname: Zhong
authors.forename: Masao
authors.initials: M
authors.lastname: Fukushima
mesh_terms: D001185:Artificial Intelligence; D002965:Classification / Q000379:methods*; D016014:Linear Models; D017711:Nonlinear Dynamics; D012984:Software