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Machine Learning Lecture 5: Theory I – PAC Learning

Machine Learning Lecture 5: Theory I – PAC Learning. Moshe Koppel Slides adapted from Tom Mitchell. To shatter n examples, we need 2 n hypotheses (since there are that many dichotomies. So the number of examples we can shatter with |H| hypotheses < log |H|.

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Machine Learning Lecture 5: Theory I – PAC Learning

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  1. Machine Learning Lecture 5: Theory I – PAC Learning Moshe Koppel Slides adapted from Tom Mitchell

  2. To shatter n examples, we need 2n hypotheses (since there are that many dichotomies. So the number of examples we can shatter with |H| hypotheses < log |H|

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