By Topic

New entropy learning method for neural network

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

4 Author(s)
Khue Hiang Chan ; Sch. of Appl. Sci., Nanyang Technol. Univ., Singapore ; Geok See Ng ; Erdogan, S.S. ; Singh, H.

An entropy penalty term is used to steer the direction of the hidden node's activation in the process of learning. A state with minimum entropy means that nodes are operating near the extreme values of the Sigmoid curve. As the training proceeds, redundant hidden nodes' activations are pushed towards their extreme value, while relevant nodes remain active in the linear region of the Sigmoid curve. The early creation of redundant nodes may impair generalisation. To prevent the network from being driven into saturation before it can really learn, an entropy cycle is proposed to dampen the early creation of such redundant nodes

Published in:

Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on  (Volume:3 )

Date of Conference:

1999