Ensembles Of LS-SVR For Time Series Prediction

被引：0
|
作者：
Li, Kunlun [1 ]
Ma, Yinghui [1 ]
Tian, Yongmei [1 ]
机构：
[1] Hebei Univ, Coll Elect & Informat Engn, Baoding 071000, Peoples R China
来源：
INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL | 2012年 / 15卷 / 11A期
基金：
中国国家自然科学基金;
关键词：
Ensemble Learning; AdaBoost; LS-SVR; Support Vector Machines;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Restricted by the over-fitting problem, it is difficult for LS-SVM learner to reach a balance between predict accuracy and generalization ability. Inspired by AdaBoost, this paper tried to extend the classical ensemble methods for LS-SVR by paying more attention to the "high fitting error areas". Compared with traditional integration learning, this approach ignored iterative processing, thus reducing the operation time as well as enhancing predict accuracy.
引用
收藏
页码：4423 / 4428
页数：6
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