Shao Yuan-Hai, Zhang Chun-Hua, Wang Xiao-Bo, Deng Nai-Yang
College of Science, China Agricultural University, Beijing, China.
IEEE Trans Neural Netw. 2011 Jun;22(6):962-8. doi: 10.1109/TNN.2011.2130540. Epub 2011 May 5.
For classification problems, the generalized eigenvalue proximal support vector machine (GEPSVM) and twin support vector machine (TWSVM) are regarded as milestones in the development of the powerful SVMs, as they use the nonparallel hyperplane classifiers. In this brief, we propose an improved version, named twin bounded support vector machines (TBSVM), based on TWSVM. The significant advantage of our TBSVM over TWSVM is that the structural risk minimization principle is implemented by introducing the regularization term. This embodies the marrow of statistical learning theory, so this modification can improve the performance of classification. In addition, the successive overrelaxation technique is used to solve the optimization problems to speed up the training procedure. Experimental results show the effectiveness of our method in both computation time and classification accuracy, and therefore confirm the above conclusion further.
对于分类问题，广义特征值近端支持向量机（GEPSVM）和孪生支持向量机（TWSVM）被视为强大支持向量机发展历程中的里程碑，因为它们使用非平行超平面分类器。在本简报中，我们基于TWSVM提出了一个改进版本，称为孪生有界支持向量机（TBSVM）。我们的TBSVM相对于TWSVM的显著优势在于，通过引入正则化项实现了结构风险最小化原则。这体现了统计学习理论的精髓，因此这种改进可以提高分类性能。此外，使用逐次超松弛技术来解决优化问题，以加快训练过程。实验结果表明了我们方法在计算时间和分类准确率方面的有效性，从而进一步证实了上述结论。
