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Pac learning and artificial neural networks

In this article, we discuss the ‘probably approximately correct’ (PAC) learning paradigm as it applies to artificial neural networks. The PAC learning model is a probabilistic framework for the study of learning and generalization. It is useful not only for neural classification problems, but also for learning problems more often associated with mainstream artificial intelligence, such as the inference of Boolean functions. In PAC theory, the notion of successful learning is formally defined using probability theory. Very roughly speaking, if a large enough sample of randomly drawn training examples is