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
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
Journal title :
Journal of Computational and Applied Mathematics
