Paper Info
Title
Hebbian iterative method for unsupervised clustering with automatic detection of the number of clusters with discrete recurrent networks
Abstract
In this paper, two important issues concerning pattern recognition 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 learning 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.
Year
DOI
Venue
2005
10.1007/11881216_26
CAEPIA
Keywords
Field
DocType
pattern cluster,network capacity,clustering task,automatic detection,pattern recognition,hebbian iterative method,patterns relationship,hebbian learning,retrieving pattern,unsupervised learning,clustering algorithm,unsupervised clustering,discrete recurrent network,pattern storage,iteration method,reinforcement learning,neural network
Competitive learning,Pattern recognition,Correlation clustering,Computer science,Consensus clustering,Unsupervised learning,Artificial intelligence,Conceptual clustering,Generalized Hebbian Algorithm,Cluster analysis,Leabra,Machine learning
Conference
Volume
ISSN
ISBN
4177
0302-9743
3-540-45914-6
Citations 
PageRank 
References 
2
0.55
5
Authors
2
Name
Order
Citations
PageRank
Enrique Mérida-Casermeiro1305.80
Domingo López-Rodríguez2559.24