23. Bagging Algorithm Let k be the number of bootstrap samples set For i =1 to k do Create a bootstrap sample D i of Size N Train a (base) classifier C i on D i End for
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26. Boosting C 1 T D 1 F (D 2 ) C 2 T D m … C m T The process of generating classifiers F
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29. AdaBoosting Algorithm The weight update mechanism (Equation): where is the normalization factor: : the weight for example ( x i , y i ) during the round
30. AdaBoosting Algorithm Let k be the number of boosting rounds, D is the set of all examples Update the weight of each examples according to Equation End for , Initialize the weights for all N examples For i = 1 to k do Create training set D i by sampling from D according to W . Train a base classifier C i on D i Apply C i to all examples in the original set D
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35. Co-Training Approach Feature Set X=(X1, X2) Classification Model One Classification Model Two new labeled data set 1 subset X1 subset X2 training training new labeled data set 2 classifying classifying Unlabeled data Unlabeled data example set L example set L
46. Step 1 Step 2 positive negative Reliable Negative (RN) Q =U - RN U P positive Using P, RN and Q to build the final classifier iteratively or Using only P and RN to build a classifier Existing 2-step strategy
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Editor's Notes
The smaller the distance between two points, the more similar