Journal
PATTERN RECOGNITION
Volume 44, Issue 10-11, Pages 2678-2692Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2011.03.031
Keywords
Support vector machine; Twin support vector machine; Nonparallel hyperplanes; Heteroscedastic noise structure; Parametric-margin model
Funding
- Shanghai Municipal Education Commission [11YZ81]
- Natural Science Foundation of SHNU [SK200937, SK201030]
- Shanghai Leading Academic Discipline Project [S30405]
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A novel twin parametric-margin support vector machine (TPMSVM) for classification is proposed in this paper. This TPMSVM, in the spirit of the twin support vector machine (TWSVM), determines indirectly the separating hyperplane through a pair of nonparallel parametric-margin hyperplanes solved by two smaller sized support vector machine (SVM)-type problems. Similar to the parametric-margin v-support vector machine (par-v-SVM), this TPMSVM is suitable for many cases, especially when the data has heteroscedastic error structure, that is, the noise strongly depends on the input value. But there is an advantage in the learning speed compared with the par-v-SVM. The experimental results on several artificial and benchmark datasets indicate that the TPMSVM not only obtains fast learning speed, but also shows good generalization. (C) 2011 Elsevier Ltd. All rights reserved.
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