Title :
L2-loss twin support vector machine for classification
Author :
Bin-Bin Gao ; Jian-Jun Wang ; Hua Huang
Author_Institution :
Sch. of Math. & Stat., Southwest Univ., Chongqing, China
Abstract :
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.
Keywords :
learning (artificial intelligence); pattern classification; quadratic programming; support vector machines; L2 loss function; L2-RTSVM; L2-loss twin support vector machine; QPP; TSVM; classification problem; coordinate descent algorithm; discriminating problems; machine learning; quadratic programming problems; regression problem; shrinking technique; dual coordinate descent; machine learning; support vector machines; twin support vector machines;
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2012 5th International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4673-1183-0
DOI :
10.1109/BMEI.2012.6513173