Improved adaptive pruning algorithm for least squares support vector regression  

作　　者：Runpeng Gao Ye San 

机构地区：[1]Control and Simulation Center,Harbin Institute of Technology,Harbin 150001,P.R.China

出　　处：《Journal of Systems Engineering and Electronics》2012年第3期438-444,共7页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China (61074127)

摘　　要：As the solutions of the least squares support vector regression machine （LS-SVRM） are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satis- factory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which in- volves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generali- zation performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance.As the solutions of the least squares support vector regression machine （LS-SVRM） are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satis- factory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which in- volves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generali- zation performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance.

关 键 词：least squares support vector regression machine （LS- SVRM） PRUNING leave-one-out （LOO） error incremental learning decremental learning. 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] P207[自动化与计算机技术—控制科学与工程]