Robust energy-based least squares twin support vector machines

Mohammad Tanveer, Mohammad Asif Khan, Shen Shyang Ho

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)174-186
Number of pages13
JournalApplied Intelligence
Volume45
Issue number1
DOIs
StatePublished - Jul 1 2016

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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