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A v–Twin Support Vector Machines based on Minimum Class Variance

Liming Yang, Xiaotong Qin

Abstract



Twin support vector machine (TWSVM) is an effective tool for data classification and regression since it solves a pair of smaller-sized quadratic programming problems (QPPs) rather than a single large one in a classical support vector machine (SVM), and thus TWSVM derives better training speed than a classical SVM. In this paper,we propose a new version of twin support vector machine, a minimum class variance -twin support vector machine (v-MCVTWSVM), to improve upon the TWSVM. This v-MCVTWSVM introduces a pair of parameters (v) to control the bounds of the fractions of the support vectors and the error
margins. Numerical experiments on some real-world databases demonstrate that the superiority of the proposed method over the classical learning paradigm in terms of both classification accuracy and the number of support vectors.

Keywords


Twin Support Vector Machine; v-support Vector Machine; Within-class Scatter Matrices

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