L2-loss twin support vector machine for classification

• Published in: 2012 5th International Conference on Biomedical Engineering and Informatics pp. 1265 - 1269
• Main Authors: Bin-Bin Gao, Jian-Jun Wang, Hua Huang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2012
• Subjects: dual coordinate descent; machine learning; support vector machines; twin support vector machines
• ISBN: 9781467311830; 1467311839
• DOI: 10.1109/BMEI.2012.6513173

Twin support vector machine (TSVM) is a rapid algorithm for resolving discriminating problems using a pair of quadratic programming problems (QPPs). Based on the TSVM and SVM, this paper proposes regularization twin support vector machine with L2 loss function (L2-RTSVM) for Classification, the coordinate descent algorithm with shrinking technique is used to solve the L2-RTSVM. L2-RTSVM has higher classification accuracy and efficiency than TSVM, and overcomes the drawback of TSVM. The experiments show that the performance of L2-RTSVM is better than those of SVM, TSVM and TPMSVM in accuracy and time.