NDLI: A multi-class large margin classifier

Content Provider	SpringerLink
Author	Tang, Liang Xuan, Qi Xiong, Rong Wu, Tie jun Chu, Jian
Copyright Year	2009
Abstract	Currently there are two approaches for a multi-class support vector classifier (SVC). One is to construct and combine several binary classifiers while the other is to directly consider all classes of data in one optimization formulation. For a K-class problem (K>2), the first approach has to construct at least K classifiers, and the second approach has to solve a much larger optimization problem proportional to K by the algorithms developed so far. In this paper, following the second approach, we present a novel multi-class large margin classifier (MLMC). This new machine can solve K-class problems in one optimization formulation without increasing the size of the quadratic programming (QP) problem proportional to K. This property allows us to construct just one classifier with as few variables in the QP problem as possible to classify multi-class data, and we can gain the advantage of speed from it especially when K is large. Our experiments indicate that MLMC almost works as well as (sometimes better than) many other multi-class SVCs for some benchmark data classification problems, and obtains a reasonable performance in face recognition application on the AR face database.
Starting Page	253
Ending Page	262
Page Count	10
File Format	PDF
ISSN	1673565X
Journal	Journal of Zhejiang University SCIENCE A
Volume Number	10
Issue Number	2
e-ISSN	18621775
Language	English
Publisher	Zhejiang University Press
Publisher Date	2009-02-01
Publisher Place	Hangzhou
Access Restriction	Subscribed
Subject Keyword	Multi-classification Support vector machine (SVM) Quadratic programming (QP) problem Large margin Mechanical Engineering Civil Engineering Classical and Continuum Physics Industrial Chemistry/Chemical Engineering
Content Type	Text
Resource Type	Article
Subject	Engineering

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