Abstract
Support vector machines (SVMs), initially proposed for two-class classification problems, have been very successful in pattern recognition problems. For multi-class classification problems, the standard hyperplane-based SVMs are made by constructing and combining several maximal-margin hyperplanes, and each class of data is confined into a certain area constructed by those hyperplanes. Instead of using hyperplanes, hyperspheres that tightly enclosed the data of each class can be used. Since the class-specific hyperspheres are constructed for each class separately, the spherical-structured SVMs can be used to deal with the multi-class classification problem easily. In addition, the center and radius of the class-specific hypersphere characterize the distribution of examples from that class, and may be useful for dealing with imbalance problems. In this paper, we incorporate the concept of maximal margin into the spherical-structured SVMs. Besides, the proposed approach has the advantage of using a new parameter on controlling the number of support vectors. Experimental results show that the proposed method performs well on both artificial and benchmark datasets.
| Original language | English |
|---|---|
| Pages (from-to) | 98-111 |
| Number of pages | 14 |
| Journal | Applied Intelligence |
| Volume | 30 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2009 Apr 1 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
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A new maximal-margin spherical-structured multi-class support vector machine. / Hao, Pei Yi; Chiang, Jung-Hsien; Lin, Yen Hsiu.
In: Applied Intelligence, Vol. 30, No. 2, 01.04.2009, p. 98-111.Research output: Contribution to journal › Article
TY - JOUR
T1 - A new maximal-margin spherical-structured multi-class support vector machine
AU - Hao, Pei Yi
AU - Chiang, Jung-Hsien
AU - Lin, Yen Hsiu
PY - 2009/4/1
Y1 - 2009/4/1
N2 - Support vector machines (SVMs), initially proposed for two-class classification problems, have been very successful in pattern recognition problems. For multi-class classification problems, the standard hyperplane-based SVMs are made by constructing and combining several maximal-margin hyperplanes, and each class of data is confined into a certain area constructed by those hyperplanes. Instead of using hyperplanes, hyperspheres that tightly enclosed the data of each class can be used. Since the class-specific hyperspheres are constructed for each class separately, the spherical-structured SVMs can be used to deal with the multi-class classification problem easily. In addition, the center and radius of the class-specific hypersphere characterize the distribution of examples from that class, and may be useful for dealing with imbalance problems. In this paper, we incorporate the concept of maximal margin into the spherical-structured SVMs. Besides, the proposed approach has the advantage of using a new parameter on controlling the number of support vectors. Experimental results show that the proposed method performs well on both artificial and benchmark datasets.
AB - Support vector machines (SVMs), initially proposed for two-class classification problems, have been very successful in pattern recognition problems. For multi-class classification problems, the standard hyperplane-based SVMs are made by constructing and combining several maximal-margin hyperplanes, and each class of data is confined into a certain area constructed by those hyperplanes. Instead of using hyperplanes, hyperspheres that tightly enclosed the data of each class can be used. Since the class-specific hyperspheres are constructed for each class separately, the spherical-structured SVMs can be used to deal with the multi-class classification problem easily. In addition, the center and radius of the class-specific hypersphere characterize the distribution of examples from that class, and may be useful for dealing with imbalance problems. In this paper, we incorporate the concept of maximal margin into the spherical-structured SVMs. Besides, the proposed approach has the advantage of using a new parameter on controlling the number of support vectors. Experimental results show that the proposed method performs well on both artificial and benchmark datasets.
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U2 - 10.1007/s10489-007-0101-z
DO - 10.1007/s10489-007-0101-z
M3 - Article
VL - 30
SP - 98
EP - 111
JO - Applied Intelligence
JF - Applied Intelligence
SN - 0924-669X
IS - 2
ER -