%A WANG Mei, ZENG Zhaohu, SUN Yingqi, YANG Erlong, SONG Kaoping %T Bayesian combination of SVR on regularization path based on KNN of input %0 Journal Article %D 2016 %J Journal of Shandong University(Engineering Science) %R 10.6040/j.issn.1672-3961.1.2016.294 %P 8-14 %V 46 %N 6 %U {http://gxbwk.njournal.sdu.edu.cn/CN/abstract/article_976.shtml} %8 2016-12-20 %X A model combination method of ε-insensitive support vector regression(ε-SVR)based on regularization path with K-Nearest Neighbor(KNN)of input was proposed. The model set was constructed with ε-SVR regularization path, which was trained by using the same original training set. The initial model set was obtained according to the piecewise linearity of SVR regularization path. The average of Bayesian Information Criterion(BIC)was applied to exclude models with poor performance and prune the initial model set. In the testing or predicting phase, the combination model set was determined with the KNN method, and Bayesian combination was performed. The pruning policy improves not only the computational efficiency of model combination but also the generalization performance. The Lε-risk consistency for model combination of ε-SVR was defined and proved, which gave the mathematical foundation of the proposed method. Experimental results demonstrated the effectiveness and efficiency of the Bayesian combination of ε-SVR on regularization path.