Cart (Loading....) | Create Account
Close category search window
 

Learning with mislabeled training samples using stochastic approximation

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)
Pathak-Pal, A. ; Electron. & Commun. Sci. Unit, Indian Stat. Inst., Calcutta, India ; Pal, S.K.

For the problem of parameter learning in pattern recognition, the convergence of stochastic approximation-based learning algorithms have been investigated for the situation in which mislabeled training samples are present. In the cases considered, it is found that estimates converge to nontrue values in the presence of labeling errors. The general m-class N-feature pattern recognition problem is considered. A possible solution to the problem is also discussed. Some simulation results are provided to support the conclusions drawn.

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:17 ,  Issue: 6 )

Date of Publication:

Nov.-Dec. 1987

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.