By Topic

Using the Karhunen-Loe've transformation in the back-propagation training algorithm

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)
H. A. Malki ; Dept. of Electr. Eng. & Comput. Sci., Wisconsin Univ., Milwaukee, WI, USA ; A. Moghaddamjoo

A novel training approach based on the back-propagation algorithm is introduced. In the proposed approach, initially, a set of training vectors is obtained by applying the Karhunen-Loe've transform on the training patterns. The training is first started in the direction of the major eigenvectors of the correlation matrix of the training patterns and then continues by gradually including the remaining components, in their order of significance. With this approach, the number of computations is significantly reduced and the learning rate is improved. The performance of this method is compared with the standard back-propagation algorithm in segmenting a synthetic noisy image

Published in:

IEEE Transactions on Neural Networks  (Volume:2 ,  Issue: 1 )