Bagging, Boosting and Ransac - LASA

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MACHINE LEARNING - 20136A concrete example•We select some input samples•We learn a regression model•Very sensitive to the input selection•m training sets = m different models(x (1)1 ,y(1) 1 ),...,(x(1) n ,y n (1) )ˆf (1) (x) =Y (1)(x (2)1 ,y(2) 1 ),...,(x(2) n ,y n (2) ) ˆf (2) (x) =Y (2)ˆf (1) , ··· ,ˆf(m)Y(1) , ··· ,Y (m)
MACHINE LEARNING - 20137Aggregation: combine several models•Linear combination of simple models•More examples = Better model•We can stop when we’re satisfiedZ = 1 mmXi=1Y (i)m = =410 60
Page 1 and 2: MACHINE LEARNING - 2013MACHINE LEAR
Page 3 and 4: 3MACHINE LEARNING - 2013The Main Id
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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
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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
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Page 33 and 34: MACHINE LEARNING - 201333Cascades o
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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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