A ridgelet kernel approach for regression using particle swarm optimization algorithm

被引：0
|
作者：
Yang, SY [1 ]
Wang, M [1 ]
Jiao, LC [1 ]
机构：
[1] Xidian Univ, Dept Elect Engn, Inst Intelligent Informat Proc, Xian 710071, Peoples R China
来源：
PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), VOLS 1-5 | 2005年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a ridgelet kernel approach is proposed for approximation of multivariate functions, especially those with certain kinds of spatial inhomogeneities. It is based on ridgelet theory, kernel and regularization technology from which we can deduce a regularized kernel regression form. Taking the objective function solved by quadratic programming to define a fitness function, we use particle swarm optimization algorithm to optimize the directions of ridgelets. Experiments in the tasks of regression prove its efficiency.
引用
收藏
页码：2837 / 2842
页数：6
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