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

The generalization error of the symmetric and scaled support vector machines

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)
Jianfeng Feng ; Sch. of Cognitive & Comput. Sci., Sussex Univ., Brighton, UK ; Williams, P.

It is generally believed that the support vector machine (SVM) optimizes the generalization error and outperforms other learning machines. We show analytically, by concrete examples in the one dimensional case, that the SVM does improve the mean and standard deviation of the generalization error by a constant factor, compared to the worst learning machine. Our approach is in terms of the extreme value theory and both the mean and variance of the generalization errors are calculated exactly for all the cases considered. We propose a new version of the SVM , called the scaled SVM, which can further reduce the mean of the generalization error of the SVM

Published in:

Neural Networks, IEEE Transactions on  (Volume:12 ,  Issue: 5 )

Date of Publication:

Sep 2001

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.