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Regression Tree Parameterization log logF x1−F x= f0 (x) + ρ 1 f 1 (x) + ⋯ + ρ L f L (x)G w1−G w = g0 w + η 1 g 1 w + ⋯ + η L g L (w) Perform functional gradient descent alternating betweenF and G Could also use EM30TAAI 201111/11/2011
Alternating Functional Gradient Descent Loss function L(F, G, y) F 0 = G 0 = f 0 = g 0 = 0 For l = 1, … , L For each site i computez i = ∂L(F l−1 x i , G l−1 , y i )/∂F l−1 x i Fit regression tree f l to x i , z i Let F l = F l−1 + ρ l f lMi=1 For each visit t to site i, computey it = ∂L F l x i , G l−1 w it , y it /∂Gl−1 w it Fit regression tree g l to w it , y it Let G l = G l−1 + η l g lM,T ii=1,t=131Hutchinson, Liu, Dietterich, AAAI 2011 TAAI 2011 11/11/2011
Page 1 and 2: Graphical Models and FlexibleClassi
Page 3 and 4: Outline Two Cultures of Machine Lea
Page 5 and 6: § 17. Allgemeine Rechtsgrundsätze
Page 7 and 8: Advantages and Disadvantages ofPara
Page 9 and 10: Another Advantage:Interactions and
Page 11 and 12: Can we combine the best of both?Pro
Page 15 and 16: Fitting logistic regression via gra
Page 17 and 18: L2-Tree Boosting Algorithm Let F 0
Page 19 and 20: Tree Boosting for Conditional Rando
Page 21 and 22: Tree Boosting for Latent Variable M
Page 23 and 24: Species Distribution ModelingObserv
Page 25 and 26: Wildlife Surveys with Imperfect Det
Page 27 and 28: Occupancy-Detection ModelOccupancyf
Page 29: Standard Approach: Log Linear (logi
Page 33 and 34: Features33TAAI 201111/11/2011
Page 35 and 36: Results for AUC of y itNo significa
Page 37 and 38: Partial Dependence PlotSynthetic Sp
Page 39 and 40: PartialDependencePlotBlue Jay vs.Ti
Page 41 and 42: Open Problems Sometimes the OD mode
Page 43 and 44: Next Steps Modeling Expertise in Ci
Page 45 and 46: Occupancy/Detectability/ExpertiseMo
Page 47 and 48: New Project: BirdCast Goal: Contine
Page 51 and 52: Combining Two Approaches toMachine

1 - Oregon State University

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