A Brief Introduction to Adaboost. Hongbo Deng 6 Feb, 2007. Some of the slides are borrowed from Derek Hoiem & Jan ˇSochman . Outline. Background Adaboost Algorithm Theory/Interpretations. What’s So Good About Adaboost. Can be used with many different classifiers
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6 Feb, 2007
Some of the slides are borrowed from Derek Hoiem & Jan ˇSochman.
Resampling for estimating statistic
Resampling for classifier design
Each training sample has a weight, which determines the probability of being selected for training the component classifier
y * h(x) = 1
y * h(x) = -1