By Topic

Weight-space probability densities and convergence times for stochastic learning

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
$33 $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)
T. K. Leen ; Dept. of Comput. Sci. & Eng., Oregon Graduate Inst. of Sci. & Technol., Beaverton, OR, USA ; G. B. Orr

The authors extend the theory of search dynamics for stochastic learning algorithms, address the time evolution of the weight-space probability density and the distribution of convergence times, with particular attention given to escape from local optima, and develop a theoretical framework that describes the evolution of the weight-space probability density. The primary results are exact predictions of the statistical distribution of convergence times for simple backpropagation and competitive learning problems

Published in:

Neural Networks, 1992. IJCNN., International Joint Conference on  (Volume:4 )

Date of Conference:

7-11 Jun 1992