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#distribution_learning_theory

Distribution learning theory

The distributional learning theory or learning of probability distribution is a framework in computational learning theory. It has been proposed from Michael Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert Schapire and Linda Sellie in 1994 and it was inspired from the PAC-framework introduced by Leslie Valiant.

Sat 16th

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Distribution learning theory Statistical learning theory Probably approximately correct learning Reinforcement learning Gumbel distribution E-learning (theory) Machine learning Outline of machine learning Beta distribution Unsupervised learning
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