Adaptive and Iterative Least Squares Support Vector Regression Based on Quadratic Renyi Entropy

被引：3
|
作者：
Jiang, Jingqing [2 ]
Song, Chuyi [2 ]
Zhao, Haiyan [2 ]
Wu, Chunguo [1 ,3 ]
Liang, Yanchun [1 ]
机构：
[1] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China
[2] Inner Mongolia Univ Nationalities, Coll Math & Comp Sci, Tongliao Inner Mongolia 028043, Peoples R China
[3] Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100080, Peoples R China
来源：
2008 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, VOLS 1 AND 2 | 2008年
基金：
美国国家科学基金会;
关键词：
D O I：
10.1109/GRC.2008.4664732
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An adaptive and iterative LSSVR algorithm based on quadratic Renyi entropy is presented in this paper. LS-SVM loses the sparseness of support vector which is one of the important advantages of conventional SVM. The proposed algorithm overcomes this drawback. The quadratic Renyi entropy is the evaluating criterion for working set selection, and the size of working set is determined at the process of iteration adoptively. The regression parameters are calculated by incremental learning and the calculation of inversing a large scale matrix is avoided. So the running speed is improved This algorithm reserves well the sparseness of support vector and improves the learning speed.
引用
收藏
页码：340 / +
页数：2
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