By Topic

Some properties of an associative memory model using the Boltzmann machine 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

3 Author(s)
T. Kojima ; Fac. of Eng., Hokkaido Univ., Sapporo, Japan ; H. Nagaoka ; T. Da-Te

In this paper, Boltzmann machine learning is applied to an associative memory model. Boltzmann machine learning is superior to both correlation learning and orthogonal learning. It is not necessary to execute this learning procedure strictly for this model. The authors examine some properties of this learning method and the associative memory model using it and try to increase the units of the network at the sacrifice of the precision of the learning.

Published in:

Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on  (Volume:3 )

Date of Conference:

25-29 Oct. 1993