Machine Learning 5: Learning Theory for Finite Hypothesis Sets, Part 1
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Machine Learning 5: Learning Theory for Finite Hypothesis Sets, Part 1
In learning theory, we give theoretical justifications for why learning algorithms focus on minimizing the number of mistakes on training data. Concretely, by assuming that training data and new unknown data are drawn as independent and identically distributed samples from an unknown distribution, it is possible to prove that the performance on ...
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