By Topic

Novel Approach to Improve the Performance of Artificial Neural Networks

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)
V. Devendran ; Department of Computer Applications, Bannari Amman Institute of Technology, Sathyamangalam, TamilNadu, India. Email: ; Hemalatha Thiagarajan ; Amitabh Wahi

Artificial neural networks, inspired by the information-processing strategies of the brain, are proving to be useful in a variety of the applications including object classification problems and many other areas of interest, can be updated continuously with new data to optimize its performance at any instant. The performance of the neural classifiers depends on many criteria i.e., structure of neural networks, initial weights, feature data, number of training samples used which are all still a challenging issues among the research community. This paper discusses a novel approach to improve the performance of neural classifier by changing the methodology of presenting the training samples to the neural classifier. The results are proving that network also depends on the methodology of giving the samples to the classifier. This work is carried out using real world dataset

Published in:

2007 International Conference on Signal Processing, Communications and Networking

Date of Conference:

22-24 Feb. 2007