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

An improved kernel Fisher discriminant classifier and its applications

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

3 Author(s)
Daqi, G. ; Dept. of Comput. Sci., East China Univ. of Sci. & Technol., Shanghai, China ; Wang Zhen ; Li Yongli

In order to use kernel Fisher discriminant (KFD) classifiers to solve large-scale learning problems, this paper decomposes an n-class dataset into n two-class subsets, and use a subset only composed of a small part of the original dataset in determining the structure of a single KFD classifier. The large number of samples in a class can be further represented by only a small number of prototypes with changeable widths, which are on behalf of kernels. Training samples are not certainly linearly separable in the kernel space, so additional expansive and contractive transformation is needed. Sigmoid functions can be use to implement such tasks. The results of two-spirals and letter recognition show that the proposed method is quite effective.

Published in:

Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on  (Volume:2 )

Date of Conference:

31 July-4 Aug. 2005

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.