Bagging, Boosting and Ransac - LASA

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MACHINE LEARNING - 20132Bootstrap Aggregation<strong>Bagging</strong>• The Main Idea• Some Examples• Why it works
3MACHINE LEARNING - 2013The Main IdeaAggregation• Imagine we have m sets of n independentobservationsS (1) ={(X 1,Y 1),...,(X n,Y n)} (1) , ... , S (m) ={(X 1,Y 1),...,(X n,Y n)} (m)all taken iid from the same underlying distribution P• Traditional approach: generate some ϕ(x,S) fromall the data samples• Aggregation: learn ϕ(x,S) by averaging ϕ(x,S (k) )over many kbeforeϕ(x,S)beforeϕ(x,S (k) )afterafter
Page 1: MACHINE LEARNING - 2013MACHINE LEAR
Page 5 and 6: MACHINE LEARNING - 20135The Main Id
Page 7 and 8: MACHINE LEARNING - 20137Aggregation
Page 9 and 10: MACHINE LEARNING - 20139In layman t
Page 11 and 12: MACHINE LEARNING - 2013Peculiaritie
Page 13 and 14: MACHINE LEARNING - 201313Adaptive B
Page 15 and 16: MACHINE LEARNING - 201315Weak Learn
Page 17 and 18: MACHINE LEARNING - 201317AdaBoostIt
Page 19 and 20: MACHINE LEARNING - 2013AdaBoostRins
Page 21 and 22: MACHINE LEARNING - 201321Boosting I
Page 23 and 24: MACHINE LEARNING - 2013Boosting In
Page 25 and 26: MACHINE LEARNING - 201325A thousand
Page 27 and 28: MACHINE LEARNING - 201327A celebrat
Page 29 and 30: MACHINE LEARNING - 201329Some simpl
Page 31 and 32: MACHINE LEARNING - 201331Some simpl
Page 33 and 34: MACHINE LEARNING - 201333Cascades o
Page 35 and 36: MACHINE LEARNING - 201335Cascades o
Page 37 and 38: MACHINE LEARNING - 201337RANSAC in
Page 39 and 40: MACHINE LEARNING - 201339Some examp
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