A SIMPLE LEMMA ON GREEDY APPROXIMATION IN HILBERT-SPACE AND CONVERGENCE-RATES FOR PROJECTION PURSUIT REGRESSION AND NEURAL NETWORK TRAINING
成果类型：
Note
署名作者：
JONES, LK
署名单位：
University of Massachusetts System; University of Massachusetts Lowell
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
DOI：
10.1214/aos/1176348546
发表日期：
1992
页码：
608-613
关键词：
摘要：
A general convergence criterion for certain iterative sequences in Hilbert space is presented. For an important subclass of these sequences, estimates of the rate of convergence are given. Under very mild assumptions these results establish an O(1/ square-root n) nonsampling convergence rate for projection pursuit regression and neural network training; where n represents the number of ridge functions, neurons or coefficients in a greedy basis expansion.