The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Pairwise classification and support vector machines
Advances in kernel methods
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Smoothing Functions for Second-Order-Cone Complementarity Problems
SIAM Journal on Optimization
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
SIAM Journal on Optimization
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Robust twin support vector machine for pattern classification
Pattern Recognition
SDP reformulation for robust optimization problems based on nonconvex QP duality
Computational Optimization and Applications
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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.