  Twin support vector machine (TWSVM) and twin support vector regression (TSVR)
are newly emerging efficient machine learning techniques which offer promising
solutions for classification and regression challenges respectively. TWSVM 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 TWSVM
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 TWSVM and TSVR simultaneously mentioning their
limitations and advantages. To begin with we first introduce the basic theory
of support vector machine, TWSVM and then focus on the various improvements and
applications of TWSVM, and then we introduce TSVR and its various enhancements.
Finally, we suggest future research and development prospects.
