In this paper, we investigate the problem of exploiting global information to improve the performance of SVMs on large scale classification problems. We first present a unified general framework for the existing min-max machine methods in terms of within-class dis- persions and between-class dispersions. By defining a new within-class dispersion measure, we then propose a novel max-margin ratio machine (MMRM) method that can be formu- lated as a linear programming problem with scalability for large data sets. Kernels can be easily incorporated into our method to address non-linear classification problems. Our empirical results show that the proposed MMRM approach achieves promising results on large data sets.

Journal of Machine Learning Research
School of Computer Science

Gu, S. (Suicheng), & Guo, Y. (2012). Max-margin ratio machine. In Journal of Machine Learning Research (Vol. 25, pp. 145–157).