Bagging, Boosting and Ransac - LASA

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MACHINE LEARNING - 20134The Main IdeaBootstrapping• Unfortunately, we usually have one singleobservations set S• Idea: bootstrap S to form the S (k) observation sets• (canonical) Choose some samples, duplicate themuntil you fill a new S (i) of the same size of S• (practical) Take a sub-set of samples of S, (use asmaller set)• The samples not used by each set are validationsamplesSS (1)S (2)S (3)
MACHINE LEARNING - 20135The Main Idea<strong>Bagging</strong>• Generate S (1) ,..., S (m) from bootstrapping• Compute the ϕ(x,S) (k) individually• Compute ϕ(x,S)=E k (ϕ(x,S) (k) ) by aggregation
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Page 3: 3MACHINE LEARNING - 2013The Main Id
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Page 11 and 12: MACHINE LEARNING - 2013Peculiaritie
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Page 15 and 16: MACHINE LEARNING - 201315Weak Learn
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Bagging, Boosting and Ransac - LASA

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