Convergence Analysis of Regression Learning Algorithm by Neural Networks
Yongquan Zhang, Feilong Cao, Tenghui Dai · 2012
This article considers the convergence rate of regression learning algorithm via the approximation property of neural networks and covering number. The upper bounds of convergence rate provided by our results is considerably tight and independent of the dimension of input space when the target function satisfies certain smooth conditions. Hence the curse of dimensionality is alleviated in these cases.