Twin support vector machine (TSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with we first introduce the basic theory of TSVM and then focus on the various improvements and applications of TSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.
翻译:双向支持矢量机(TSVM)和双向支持矢量回归(TSVR)是新出现的高效机器学习技术,分别为分类和回归挑战提供有希望的解决方案。TSVM基于一种想法,即确定将数据点分类到各自分类的两种非平行超天体;它需要解决两个小型的二次编程问题,而不是解决用于支持矢量机(SVM)的单一大号QPP(QPP),而TSVR则在TSVM线上制定,并需要解决两个SVM型问题。虽然这些技术的研究进展良好;关于对TSVR不同变体进行比较的文献有限。因此,本审查对最近对TSVM和TSVR的研究进行了严格分析,同时提到其局限性和优势。首先我们介绍TSVM的基本理论,然后侧重于TSVM的各种改进和应用,然后我们介绍TSVR及其各种强化。最后,我们建议未来的研究与发展前景。