A New Support Vector Machine for Multi-class Classification
Naiyang Deng, Zhiquan Qi, Yingjie Tian · 2005
Support vector machines (SVMs) for classification - in short SVC - 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 nu-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 support vector classification-regression (K-LSVCR). Numerical experiments on artificial data sets and benchmark data sets show that the proposed method is almost as efficient as K-SVCR and nu-K-SVCR, while considerably faster than them