By Topic

Supervised fuzzy ART: training of a neural network for pattern classification via combining supervised and unsupervised learning

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)
Hahn-Ming Lee ; Dept. of Electron. Eng., Nat. Taiwan Inst. of Technol., Taipei, Taiwan ; Lai, C.-S.

A neural network model that incorporates a supervised mechanism into a fuzzy automated reasoning tool (ART) is presented. In any time, the training instances may or may not have desired outputs, that is, this model can handle supervised learning and unsupervised learning simultaneously. The unsupervised component finds the cluster relations of instances. Then the supervised component learns the desired associations between clusters and categories. This model has the ability of incremental learning. It works equally well when instances in a cluster belong to different categories. Multicategory and nonconvex classifications can also be dealt with

Published in:

Neural Networks, 1993., IEEE International Conference on

Date of Conference: