By Topic

Neural modeling of piecewise linear classifiers

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
$33 $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)
M. Vriesenga ; GDE Systems Inc., San Diego, CA, USA ; J. Sklansky

We show that every piecewise linear classifier can be constructed as a three-layer network of linear decision functions. We define neural modeling as the replacement and subsequent training of each of these linear decision functions by a formal neuron with a differentiable activation function. We show that neural modeling of a piecewise linear classifier provides a means of combining the economy of design of piecewise linear classifiers with the good generalizing ability and low error rates of well designed neural classifiers. We show that globally optimized neural classifiers can be obtained from neural modeling of genetically designed piecewise linear classifiers. We describe applications of these techniques to an artificial data set and to a detector of lines and edges in a noisy aerial image

Published in:

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

Date of Conference:

25-29 Aug 1996