关键词: 机器学习, 支持向量回归机（SVR）, 双支持向量回归机（TSVR）

Abstract: This paper proposes a nonparallel ε-band support vector regression (NSVR) method, in which an ε-up hyperplane and an ε-down hyperplane are constructed via a pair of convex quadratic programming (QP) problems and the final regression function is derived. However, it is worth mentioning that the size of each QP problem is a half that of support vector regression (SVR). Compared with twin support vector regression (TSVR), although they are both looking for two nonparallel hyperplanes to construct the final regression function, the geometrical meaning of NSVR is more close to SVR. The experimental results indicate that NSVR has not only a good generalization performance but also a high speed.

Key words: machine learning, support vector regression (SVR), twin support vector regression (TSVR)