Stochasticity applied to a neural tree classifier

This paper describes various mechanisms for adding stochasticity to a dynamic hierarchical neural clusterer. Such a network grows a tree-structured neural classifier dynamically in response to the unlabelled data with which it is presented. Experiments are undertaken to evaluate the effects of this addition of stochasticity. These tests were carried out using two sets of internal parameters, that define the characteristics of the neural clusterer A genetic algorithm using appropriate cluster criterion measures in its fitness function was used to search the parameter space for these instantiations. It was found that the addition of nondeterminism produced more reliable clustering performances especially on unseen real world data