A ν-twin support vector machine based regression with automatic accuracy control

摘要

This paper presents an efficient ν-Twin Support Vector Machine Based Regression Model with Automatic Accuracy Control (ν-TWSVR). This ν-TWSVR model is motivated by the celebrated ν-SVR model (Schlkoff et al. 1998) and recently introduced ?-TSVR model (Shao et al., Neural Comput Applic 23(1):175–185, 2013). The ν-TSVR model can automatically optimize the parameters ? 1 and ? 2 according to the structure of the data such that at most certain specified fraction ν 1(respectively ν 2) of data points contribute to the errors in up (respectively down) bound regressor. The ν-TWSVR formulation constructs a pair of optimization problems which are mathematically derived from a related ν-TWSVM formulation (Peng, Neural Netw 23(3):365–372, 2010) and making use of an important result of Bi and Bennett (Neurocomputing 55(1):79–108, 2003). The experimental results on artificial and UCI benchmark datasets show the efficacy of the proposed model in practice.