Classification multi-classe au prix d’un classifieur binaire

This paper deals with the problem of multi-class classification in machine learning. Various techniques have been successfully proposed to solve such problems, with a computation cost often much higher than techniques dedicated to binary classification. To address this problem, we propose a novel formulation for designing multi-class classifiers, with essentially the same computational complexity as binary classifiers. The proposed approach provides a framework to develop multi-class algorithms using the same optimization routines as those already available for binary classification tasks. The effectiveness of our approach is illustrated with Support Vector Machines (SVM), Least-Squares SVM (LS-SVM), and Regularized Least Squares Classification (RLSC).