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.

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