Abstract
A discussion is presented of the requirements of learning for generalization, which is NP-complete and cannot be addressed by traditional methods based on gradient descent. The authors present a stochastic learning algorithm based on simulated annealing in weight space and discuss stopping criteria for the algorithm, to avoid overfitting of learning examples. >
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