Max-Margin Ratio Machine

Suicheng Gu, Yuhong Guo
; Proceedings of the Asian Conference on Machine Learning, PMLR 25:145-157, 2012.

Abstract

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 dispersions 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 formulated 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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v25-gu12, title = {Max-Margin Ratio Machine}, author = {Suicheng Gu and Yuhong Guo}, booktitle = {Proceedings of the Asian Conference on Machine Learning}, pages = {145--157}, year = {2012}, editor = {Steven C. H. Hoi and Wray Buntine}, volume = {25}, series = {Proceedings of Machine Learning Research}, address = {Singapore Management University, Singapore}, month = {04--06 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v25/gu12/gu12.pdf}, url = {http://proceedings.mlr.press/v25/gu12.html}, abstract = {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 dispersions 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 formulated 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.} }
Endnote
%0 Conference Paper %T Max-Margin Ratio Machine %A Suicheng Gu %A Yuhong Guo %B Proceedings of the Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2012 %E Steven C. H. Hoi %E Wray Buntine %F pmlr-v25-gu12 %I PMLR %J Proceedings of Machine Learning Research %P 145--157 %U http://proceedings.mlr.press %V 25 %W PMLR %X 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 dispersions 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 formulated 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.
RIS
TY - CPAPER TI - Max-Margin Ratio Machine AU - Suicheng Gu AU - Yuhong Guo BT - Proceedings of the Asian Conference on Machine Learning PY - 2012/11/17 DA - 2012/11/17 ED - Steven C. H. Hoi ED - Wray Buntine ID - pmlr-v25-gu12 PB - PMLR SP - 145 DP - PMLR EP - 157 L1 - http://proceedings.mlr.press/v25/gu12/gu12.pdf UR - http://proceedings.mlr.press/v25/gu12.html AB - 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 dispersions 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 formulated 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. ER -
APA
Gu, S. & Guo, Y.. (2012). Max-Margin Ratio Machine. Proceedings of the Asian Conference on Machine Learning, in PMLR 25:145-157

Related Material