By Topic

Concept learning from visual experiences using unsupervised 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
$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)
Jeewanee Bamunusinghe ; Clayton School of Information Technology, Monash University, Melbourne, Australia ; Damminda Alahakoon

Ability of learning and understanding are essential for intelligent systems. Concept formation is a way of representing knowledge in machines as humans do. To achieve true intelligence in machines there is a necessity of developing techniques which can accumulate knowledge from a given sequence of input patterns without human intervention. Feigenbaum's EPAM, Fisher's COBWEB and Lebowitz's UNIMEM are some of the existing models for concept formation. These techniques use a decision tree for concept formation which perform a number of tests for each input. Therefore they have several shortcomings such as instance arrival order, processing time and ad hoc nature of selecting attributes for tests. This paper describe a technique which extracts several common features of the existing concept formation techniques and demonstrates a novel method of extracting concepts from visual experiences using an unsupervised neural network which addresses the above mentioned shortcomings. An unsupervised neural network called Growing Self Organizing Map is used to obtain the clusters from the data set and then a statistical analysis is carried out for extracting the contributing attributes for concepts.

Published in:

2007 Third International Conference on Information and Automation for Sustainability

Date of Conference:

4-6 Dec. 2007