Smooth Twin Parametric Insensitive Support Vector Regression
[1]HUANG Huajuan,WEI Xiuxi,ZHOU Yongquan.Smooth Twin Parametric Insensitive Support Vector Regression[J].Journal of Zhengzhou University (Engineering Science),2022,43(02):28-34.[doi:10.13705/j.issn.1671-6833.2022.02.005]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
43
Number of periods:
2022 02
Page number:
28-34
Column:
Public date:
2022-02-27
- Title:
- Smooth Twin Parametric Insensitive Support Vector Regression
- Keywords:
- twin parametric insensitive support vector regression; smooth technology; heteroscedastic noise; Newton method; training efficiency
- CLC:
- TP18
- Abstract:
- As one of the machine learning methods, twin parametric insensitive support vector regression (TPISVR) had a simple mathematical model and good learning performance. It was especially suitable for solving data regression problems with structural heteroscedasticity noise. However, the training speed of TPISVR was low, and the training efficiency needs to be improved. The traditional algorithm of TPISVR could be reduced to solve two quadratic programming problems with inequality constraints by transforming dual problems. However, this method of solving quadratic programming problems with large number of samples would be restricted by time and memory, which was the key to the low training efficiency of TPISVR. In this study, the positive sign function was introduced to transform the two quadratic programming problems of TPISVR into two non-differentiable unconstrained optimization problems. Secondly, CHKS smooth function and regular term were introduced to regularize TPISVR model, and smooth approximation was made to the non-differentiable unconstrained optimization problem, so as to transform the non-differentiable model into a differentiable unconstrained optimization problem. The new model was solved by Newton Armijo method with fast convergence speed, and a smooth twin parameterized insensitive support vector regression machine (STPISVR) was proposed. Finally, it was proved theoretically that STPISVR model was convergent and had arbitrary order smoothness; In order to verify the effectiveness and feasibility of the algorithm, simulation experiments were carried out on the artificial data set and UCI data set commonly used in machine learning. The experimental results showed that compared with other machine learning methods, STPISVR achieved higher training efficiency without reducing the accuracy.
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Last Update:
2022-02-25