By Topic

Constructive and robust combination of perceptrons

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)
Eigenmann, R. ; Lehrstuhl fur Netzwerktheorie und Schaltungstech., Tech. Univ. Munchen, Germany ; Nossek, J.A.

We propose a new strategy for a constructive training of feedforward neural networks to classify linearly nonseparable patterns. The algorithm results in a configuration of the first layer of the network, which is able to give a faithful internal representation of the input patterns. The weights of the network are obtained by the CadaTron algorithm introduced, which is able to separate clusters of data in a robust way. Iteratively, further neurons are added to the neural net in order to decrease the training error. Unnecessary neurons are removed, so this algorithm leads to a network with low complexity and excellent generalization properties. The results of this work are based on the classification of handwritten characters

Published in:

Pattern Recognition, 1996., Proceedings of the 13th International Conference on  (Volume:4 )

Date of Conference:

25-29 Aug 1996