Novel Robust Twin Support Vector Regression

Abstract

Regression problem is one of the basic problems in the field of pattern recognition and machine learning. Twin support vector regression (TSVR) is a new algorithm to deal with regression problems developed on the basis of support vector regression (SVR). It has good performance in dealing with noiseless data, but poor performance in dealing with noisy data.?In order to reduce the influence of noise on the performance of TSVR, the mixed Hε loss function is constructed by combining ε -insensitive loss function and Huber loss function, which can be effectively adapted to the noise of different distributions. Then, a robust twin support vector regression (Hε-TSVR) is proposed based on the mixed [Hε] loss function and the principle of structural risk minimization (SRM), and the model is solved by Newton iterative method in the primal space. Experiments are carried out on some noisy and noiseless artificial datasets and UCI datasets respectively, and the experimental results verify the effectiveness of the proposed algorithm compared with SVR and TSVR, etc.

Keywords

• pattern recognition; support vector regression (svr); twin support vector regression (tsvr); loss function