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A ν-twin support vector machine based regression with automatic accuracy control
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DOI：10.1007/s10489-016-0860-5.png)
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Abstract
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This paper presents an efficient nu-Twin Support Vector Machine Based Regression Model with Automatic Accuracy Control (nu-TWSVR). This nu-TWSVR model is motivated by the celebrated nu-SVR model (Schlkoff et al. 1998) and recently introduced is an element of-TSVR model (Shao et al., Neural Comput Applic 23(1):175-185, 2013). The nu-TSVR model can automatically optimize the parameters is an element of(1) and is an element of(2) according to the structure of the data such that at most certain specified fraction nu(1)(respectively nu(2)) of data points contribute to the errors in up (respectively down) bound regressor. The nu-TWSVR formulation constructs a pair of optimization problems which are mathematically derived from a related nu-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.
Keywords:
Support vector machine
Regression
Twin support vector machine
Twin support vector regression
Support vectors
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