A novel parametric-insensitive nonparallel support vector machine for regression

In this paper, a novel parametric-insensitive nonparallel support vector regression (PINSVR) algorithm for data regression is proposed. PINSVR indirectly finds a pair of nonparallel proximal functions with a pair of different parametric-insensitive nonparallel proximal functions by solving two smaller sized quadratic programming problems (QPPs). By using new parametric-insensitive loss functions, the proposed PINSVR automatically adjusts a flexible parametric-insensitive zone of arbitrary shape and minimal size to include the given data to capture data structure and boundary information more accurately. The experiment results compared with the ε-SVR, ε-TSVR, and TPISVR indicate that our PINSVR not only obtains comparable regression performance, but also obtains better bound estimations.