An Approach to Artificial Concept Learning Based on Human Concept Learning by Using Artificial Neural Networks - Advancing Artificial Intelligence Through Biological Process Applications - page 131

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Chapter VIII
An Approach to Artificial
Concept Learning Based on
Human Concept Learning by
Using Artificial Neural Networks

Enrique Mérida-Casermeiro
University of Málaga, Spain

Domingo López-Rodríguez
University of Málaga, Spain

J.M. Ortiz-de-Lazcano-Lobato
University of Málaga, Spain

AbSTRACT

In this chapter, two important issues concerning associative memory by neural networks are studied: a
new model of hebbian learning, as well as the effect of the network capacity when retrieving patterns
and performing clustering tasks. Particularly, an explanation of the energy function when the capacity
is exceeded: the limitation in pattern storage implies that similar patterns are going to be identified by
the network, therefore forming different clusters. This ability can be translated as an unsupervised learn-
ing of pattern clusters, with one major advantage over most clustering algorithms: the number of data
classes is automatically learned, as confirmed by the experiments. Two methods to reinforce learning are
proposed to improve the quality of the clustering, by enhancing the learning of patterns relationships.
As a related issue, a study on the net capacity, depending on the number of neurons and possible outputs,
is presented, and some interesting conclusions are commented.