Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Binary classification by SVM based tree type neural networks

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)
Jayadeva ; Dept. of Electr. Eng., Indian Inst. of Technol., New Delhi, India ; Deb, A.K. ; Chandra, S.

A technique for building a multilayer perceptron classifier network is presented. Initially, a single perceptron tries to correctly classify as many samples as possible. Misclassified samples are taken care of by adding as bias the output of up to two neurons to the parent neuron. The final classification boundary between the two disjoint half spaces at the output of the parent neuron is determined by a maximum margin classifier type SVM applied jointly to the training set of the parent neuron along with the correcting inputs from its child neuron(s). The growth of a branch in the network ceases when the terminal neuron is able to correctly classify all samples from its training set. No a priori assumptions need to be made regarding the number of neurons in the network or the kernel of the SVM classifier. Examples are presented to illustrate the effectiveness of the technique

Published in:

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on  (Volume:3 )

Date of Conference: