By Topic

Convergence analysis of stochastic pseudo-gradient algorithms and application to learning in feedforward neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Tadic, V. ; Autom. Control Lab., Mihajlo Pupin Inst., Belgrade, Serbia ; Stankovic, S.

The convergence of a class of stochastic pseudo-gradient algorithms driven by correlated data sequences is considered in this paper. The obtained results are applied to a learning algorithm for feedforward neural networks and sufficient conditions for its convergence are determined

Published in:

Information Theory. 1997. Proceedings., 1997 IEEE International Symposium on

Date of Conference:

29 Jun-4 Jul 1997