Bagging, Boosting and Ransac - LASA

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MACHINE LEARNING - 201314The Main IdeaIterative Approach• Combine several simple models (weak learners)• Avoid redundancy• Each learner complements the previous ones• Keep track of the errors of the previous learners
MACHINE LEARNING - 201315Weak Learners• A “simple” classifier that can be generated easily• As long as it is better than r<strong>and</strong>om, we can use it• Better when tailored to the problem at h<strong>and</strong>• E.g. very fast at retrieval (for images)
Page 1 and 2: MACHINE LEARNING - 2013MACHINE LEAR
Page 3 and 4: 3MACHINE LEARNING - 2013The Main Id
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: MACHINE LEARNING - 201313Adaptive B
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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Bagging, Boosting and Ransac - LASA

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