Title of article
Convergence of an online gradient method for feedforward neural networks with stochastic inputs
Author/Authors
Li، نويسنده , , Zhengxue and Wu، نويسنده , , Wei and Tian، نويسنده , , Yulong، نويسنده ,
Issue Information
روزنامه با شماره پیاپی سال 2004
Pages
12
From page
165
To page
176
Abstract
In this paper, we study the convergence of an online gradient method for feed-forward neural networks. The input training examples are permuted stochastically in each cycle of iteration. A monotonicity and a weak convergence of deterministic nature are proved.
Keywords
Online gradient method , Stochastic inputs , Feedforward neural networks , Convergence
Journal title
Journal of Computational and Applied Mathematics
Serial Year
2004
Journal title
Journal of Computational and Applied Mathematics
Record number
1552433
Link To Document