GENERALIZATION BY NEURAL NETWORKS

Neural networks have traditionally been applied to recognition problems, and most learning algorithms are tailored to those problems. We discuss the requirements of learning for generalization, where the traditional methods based on gradient descent have limited success. We present a new stochastic learning algorithm based on simulated annealing in weight space. We verify the convergence properties and feasibility of the algorithm. We also describe an implementation of the algorithm and validation experiments.