On the efficiency of the orthogonal least squares training method for radial basis function networks

被引：83
|
作者：
Sherstinsky, A
Picard, RW
机构：
[1] MIT Media Lab., Massachusetts Institute of Technology, Cambridge
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 1996年 / 7卷 / 01期
关键词：
D O I：
10.1109/72.478404
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The efficiency of the orthogonal least squares (OLS) method for training approximation networks is examined using the criterion of energy compaction. We show that the selection of basis vectors produced by the procedure is not the most compact when the approximation is performed using a nonorthogonal basis. Hence, the algorithm does not produce the smallest possible networks for a given approximation error. Specific examples are given using the Gaussian radial basis functions (RBF's) type of approximation networks.
引用
收藏
页码：195 / 200
页数：6
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