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 ?-SVR, ?-TSVR, and TPISVR indicate that our PINSVR not only obtains comparable regression performance, but also obtains better bound estimations.
support vector machine bound estimation regression estimation noise heteroscedastic model parametric insensitive model