TY - JOUR
AU1 - Tanveer, Mohammad
AU2 - Khan, Mohammad
AU3 - Ho, Shen-Shyang
AB - Twin support vector machine (TSVM), least squares TSVM (LSTSVM) and energy-based LSTSVM (ELS-TSVM) satisfy only empirical risk minimization principle. Moreover, the matrices in their formulations are always positive semi-definite. To overcome these problems, we propose in this paper a robust energy-based least squares twin support vector machine algorithm, called RELS-TSVM for short. Unlike TSVM, LSTSVM and ELS-TSVM, our RELS-TSVM maximizes the margin with a positive definite matrix formulation and implements the structural risk minimization principle which embodies the marrow of statistical learning theory. Furthermore, RELS-TSVM utilizes energy parameters to reduce the effect of noise and outliers. Experimental results on several synthetic and real-world benchmark datasets show that RELS-TSVM not only yields better classification performance but also has a lower training time compared to ELS-TSVM, LSPTSVM, LSTSVM, TBSVM and TSVM.
TI - Robust energy-based least squares twin support vector machines
JF - Applied Intelligence
DO - 10.1007/s10489-015-0751-1
DA - 2016-02-04
UR - https://www.deepdyve.com/lp/springer-journals/robust-energy-based-least-squares-twin-support-vector-machines-DPlfUQqh4N
SP - 174
EP - 186
VL - 45
IS - 1
DP - DeepDyve
ER -