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摘要
En 中文
In a twin support vector machine (TSVM), the separating hyperplane associated with one class is determined such that the hyperplane is near to the data belonging to the class and that it is away from the other class. A data sample is classified into the class with the nearer hyperplane. In this paper we discuss whether the twin hyperplanes are necessary for the TSVM. By theoretical analysis we first show the equivalence conditions that one of the two decision boundaries of the TSVM coincides with the decision boundary of the SVM. Then for the least squares (LS) version of the TSVM, we clarify the equivalence conditions with the LS SVM or that with two hyperparameters for imbalanced data (one for each class). A comparison of the LS TSVM with the LS SVMs, by computer experiments, shows that the generalization abilities of the LS TSVM are comparable but not superior for 13 two-class problems and an imbalanced two-class problem. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
SUPPORT VECTOR MACHINES
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3.3
论文数:
8.0K
被引数:
1.6W
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