The key assumption of this paper is that categorization can he related to the statistical problem of probability density estimation. Ashby and Alfonso-Reese (1995) have shown that several existing models of categorization can be related to specific statistical methods of density estimation. I extend this work in two ways. First, I show how a semi-parametric statistical technique of density estimation based on a Gaussian mixture distribution, can be used to construct a new model of categorization called the general decision bound model. Second, I propose a neural network framework based on RBF networks with its statistical interpretation. This framework is used to construct a neural network implementation of both the Gaussian and the general decision bound models.
How to Cite:
Rosseel, Y., 1996. Connectionist Models of Categorization: A Statistical Interpretation. Psychologica Belgica, 36(1-2), pp.93–112. DOI: http://doi.org/10.5334/pb.895