Learning parsimonious dendritic classifiers

From a practical industrial point of view parsimonious classifiers based on dendritic computing (DC) have two advantages: First they are implemented using only additive and min/max operators. They can be implemented in simple processors and be extremely fast providing classification responses. Second, parsimonious models improve generalization. In this paper we develop a formulation of dendritic classifiers based on lattice kernels and we train them using a direct Monte Carlo approach and a Sparse Bayesian Learning. We compare the results of both kinds of training with the relevance vector machines (RVM) on a collection of benchmark datasets.