Learning optimal conjunctive concepts through a team of stochastic automata

The problem of learning conjunctive concepts from a series of positive and negative examples of the concept is considered. Employing a probabilistic structure on the domain, the goal of such inductive learning is precisely characterized. A parallel distributed stochastic algorithm is presented. It is proved that the algorithm will converge to the concept description with maximum probability of correct classification in the presence of up to 50% unbiased noise. A novel neural network structure that implements the learning algorithm is proposed. Through empirical studies it is seen that the algorithm is quite efficient for learning conjunctive concepts