![]() A decision-theoretic generalization of on-line learning and an application to boosting , 1995 The first realization of boosting that saw great success in application was Adaptive Boosting or AdaBoost for short.īoosting refers to this general problem of producing a very accurate prediction rule by combining rough and moderately inaccurate rules-of-thumb. Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World, page 152, 2013 AdaBoost the First Boosting Algorithm … Note, however, it is not obvious at all how this can be done ![]() The idea is to use the weak learning method several times to get a succession of hypotheses, each one refocused on the examples that the previous ones found difficult and misclassified. Hypothesis boosting was the idea of filtering observations, leaving those observations that the weak learner can handle and focusing on developing new weak learns to handle the remaining difficult observations. These ideas built upon Leslie Valiant’s work on distribution free or Probably Approximately Correct (PAC) learning, a framework for investigating the complexity of machine learning problems. Thoughts on Hypothesis Boosting , 1988Ī weak hypothesis or weak learner is defined as one whose performance is at least slightly better than random chance. … an efficient algorithm for converting relatively poor hypotheses into very good hypotheses Michael Kearns articulated the goal as the “ Hypothesis Boosting Problem” stating the goal from a practical standpoint as: The idea of boosting came out of the idea of whether a weak learner can be modified to become better.
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