Abstract
In this paper, an information-theoretic-based adaptive resonance theory (IT-ART) neural network architecture is presented. Each IT-ART category is defined by the first and second order statistics (mean and covariance matrix) of the cluster or class it represents. This information is used to estimate probability density functions (multivariate Gaussians) and compute the activation functions. The match function of the vigilance check is based on Renyi's quadratic cross-entropy: it is the cross information potential. Experiments involving several real world and synthetic data sets were carried out to assess the performance of IT-ART, which was measured in terms of external validity indices. IT-ART expanded the range of successful vigilance parameter values in these tests.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call