A nu-twin support vector machine (nu-TSVM) classifier and its geometric algorithms
A nu-twin support vector machine (nu-TSVM) classifier and its geometric algorithms
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DOI：
10.1016/j.ins.2010.06.039
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发表时间：
2010-10
期刊：
影响因子：
--
通讯作者：
X. Peng
中科院分区：
文献类型：
--
作者：
X. Peng
本文提出了一种孪生支持向量机(ν-ν)，对最近提出的孪生支持向量机进行了改进。该ν-TSVM引入一对参数(ν)来控制支持向量分数的界和误差范围。理论分析表明，这种ν-T-S支持向量机可以被解释为两个约化凸壳上的一对极小广义马氏范数问题。在著名的Gilbert算法的基础上，提出了一种几何算法(GA-TSVM)及其概率加速算法PGA-TSVM。在几个合成数据集和基准数据集上的计算结果表明，所提出的算法在计算复杂度和分类精度方面都具有明显的优势。
In this paper, a ν-twin support vector machine (ν-TSVM) is presented, improving upon the recently proposed twin support vector machine (TSVM). This ν-TSVM introduces a pair of parameters (ν) to control the bounds of the fractions of the support vectors and the error margins. The theoretical analysis shows that this ν-TSVM can be interpreted as a pair of minimum generalized Mahalanobis-norm problems on two reduced convex hulls (RCHs). Based on the well-known Gilbert’s algorithm, a geometric algorithm for TSVM (GA-TSVM) and its probabilistic speed-up version, named PGA-TSVM, are presented. Computational results on several synthetic as well as benchmark datasets demonstrate the significant advantages of the proposed algorithms in terms of both computation complexity and classification accuracy.