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In [[theoretical computer science]], '''computational learning theory''' or computational learning problem is a mathematical field related to the analysis of [[machine learning]] algorithms. it is traditionally referred to as [[grammatical inference]] problem.
Machine learning algorithms take a training set, form hypotheses or models, and make predictions about the future. Because the training set is finite and the future is uncertain, learning theory usually does not yield absolute guarantees of performance of the algorithms. Instead, probabilistic bounds on the performance of machine learning algorithms are quite common.
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