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

On exponential bounds on the Bayes risk of the kernel classification rule

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

1 Author(s)
Krzyzak, A. ; Dept. of Comput. Sci., Concordia Univ., Montreal, Que., Canada

The exponential, distribution-free bounds for the kernel classification rule are derived. The equivalence of all modes of the global convergence of the rule is established under optimal assumptions on the smoothing sequence. Also derived is the optimal global rate of convergence of the kernel regression estimate within the class of Lipschitz distributions. The rate is optimal for the nonparametric regression, but not for classifications. It is shown. using the martingale device, that weak, strong, and complete L1 Bayes risk consistencies are equivalent. Consequently the conditions on the smoothing sequence hn to 0 and nhn to infinity are necessary and sufficient for Bayes risk consistency of the kernel classification rule. The rate of convergence of the kernel classification rule is also given.

Published in:

Information Theory, IEEE Transactions on  (Volume:37 ,  Issue: 3 )

Date of Publication:

May 1991

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.