Twin support vector regression (TSVR) was proposed recently as a novel regressor that tries to find a pair of nonparallel
planes, i.e., ε-insensitive up- and down-bounds, by solving two related SVM-type problems. However, it may incur suboptimal
solution since its objective function is positive semi-definite and the lack of complexity control. In order to address this
shortcoming, we develop a
... [Show full abstract] novel SVR algorithm termed as smooth twin SVR (STSVR). The idea is to reformulate TSVR as a strongly
convex problem, which results in unique global optimal solution for each subproblem. To solve the proposed optimization problem,
we first adopt a smoothing technique to convert the original constrained quadratic programming problems into unconstrained
minimization problems, and then use the well-known Newton–Armijo algorithm to solve the smooth TSVR. The effectiveness of
the proposed method is demonstrated via experiments on synthetic and real-world benchmark datasets.
KeywordsMachine learning–Support vector regression–Nonparallel planes–Smoothing techniques