An effective algorithm for inverse problem of SVM based on MM algorithm

Author

Zhu, Jie ; Li, Run-ya ; Wu, Shu-fang ; JI, SONG ; LI, MAN

Author_Institution

Dept. of Inf. Manage., Central Inst. for Correctional Police, Baoding, China

Volume

2

fYear

2009

fDate

12-15 July 2009

Firstpage

1000

Lastpage

1004

Abstract

This paper investigates an effective algorithm for inverse problem of support vector machines. The inverse problem is how to split a given dataset into two clusters such that the margin between the two clusters attains maximum. However the training time for inverse problem of SVM is incredible. Clustering is a feasible way to simplify the process of it, but it is difficult to estimate the number of the clusters. In this paper, we design a margin-merging cluster algorithm to solve this problem. We compare our approach with the k-means solution in terms of accuracy loss and training time. Simulations show that the proposed algorithm can solve it efficiently.

Keywords

inverse problems; pattern clustering; support vector machines; MM algorithm; SVM; inverse problem; k-mean solution; margin-merging cluster algorithm; simulation; support vector machines; Algorithm design and analysis; Clustering algorithms; Conference management; Cybernetics; Inverse problems; Machine learning; Machine learning algorithms; Software algorithms; Support vector machine classification; Support vector machines; Margin-merging(mm) Cluster; Precision; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2009 International Conference on

Conference_Location

Baoding

Print_ISBN

978-1-4244-3702-3

Electronic_ISBN

978-1-4244-3703-0

Type

conf

DOI

10.1109/ICMLC.2009.5212455

Filename

5212455