By Topic

Linear classifiers in perceptron design

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)
Raudys, S. ; Inst. of Math. & Inf., Vilnius, Lithuania

It is shown adaptive training of the nonlinear single layer perceptron can lead to seven different statistical classifiers: (1) Euclidean distance classifier; (2) standard Fisher linear discriminant function; (3) Fisher linear discriminant function, with pseudoinverse of the covariance matrix; (4) regularised discriminant analysis; (5) generalised Fisher discriminant function; (6) minimum empirical error classifier; and (7) maximum margin classifier and to intermediate ones. Which particular type of the classifier will be obtained depends on: 1) initialisation interval and its relation to the training data; 2) an initial value of the learning step; and 3) its change during the iteration process, the stopping criteria

Published in:

Pattern Recognition, 1996., Proceedings of the 13th International Conference on  (Volume:4 )

Date of Conference:

25-29 Aug 1996