L2-loss twin support vector machine for classification

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.

[1]  Jianjun Wang,et al.  Margin calibration in SVM class-imbalanced learning , 2009, Neurocomputing.

[2]  Marimuthu Palaniswami,et al.  Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings , 2009, IEEE Transactions on Information Technology in Biomedicine.

[3]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[5]  Madan Gopal,et al.  Application of smoothing technique on twin support vector machines , 2008, Pattern Recognit. Lett..

[6]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[7]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[8]  Madan Gopal,et al.  Least squares twin support vector machines for pattern classification , 2009, Expert Syst. Appl..

[9]  Lipo Wang,et al.  Data Mining With Computational Intelligence , 2006, IEEE Transactions on Neural Networks.

[10]  Xinjun Peng,et al.  TPMSVM: A novel twin parametric-margin support vector machine for pattern recognition , 2011, Pattern Recognit..

[11]  Yuan-Hai Shao,et al.  A coordinate descent margin based-twin support vector machine for classification , 2012, Neural Networks.

[12]  Yurii Nesterov,et al.  Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems , 2012, SIAM J. Optim..

[13]  David Burshtein,et al.  Support Vector Machine Training for Improved Hidden Markov Modeling , 2008, IEEE Transactions on Signal Processing.

[14]  Yuan-Hai Shao,et al.  Improvements on Twin Support Vector Machines , 2011, IEEE Transactions on Neural Networks.

[15]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[16]  Chih-Jen Lin,et al.  A dual coordinate descent method for large-scale linear SVM , 2008, ICML '08.

[17]  Reshma Khemchandani,et al.  Twin Support Vector Machines for Pattern Classification , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[19]  Alexander J. Smola,et al.  Learning with kernels , 1998 .