Abstract
Motivated by nonparallel hyperplanes support vector machine (NHSVM), a new regression method of data, named as nonparallel hyperplanes support vector regression (NHSVR), is proposed in this paper. The advantages of NHSVR have two aspects, one is considering the minimization of structure risk by introducing a regularization term in objective function, and another is finding two nonparallel hyperplanes by solving a combined quadratic programming problem. In order to verify the effectiveness of the propose method, a series of comparative experiments are performed with TSVR, LTSVR, and TSVR e on five evaluation indexes. The experiment results show that the proposed NHSVR is an effective and efficient algorithm for regression of data.
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