1 - Oregon State University

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L2-TreeBoosting can be applied to any fullyobserveddirected graphical model P(Y 1 |X 1 ) as sum of trees P(Y 2 |X 1 , X 2 ) as sum of treesX 1 X 2 What about undirected graphicalmodels?Y 1Y 218TAAI 201111/11/2011
Tree Boosting for Conditional RandomFields Conditional Random Field(Lafferty et al., 2001)Y 1Y 2…Y T P(Y 1 , … , Y T |X 1 , … , X T ) Undirected graph over theY’s conditioned on the X’s. Φ(Y t−1 , Y t , X) = log linearmodelX 1X 2…X T Dietterich, Hao, Ashenfelter (JMLR 2008; ICML 2004) Fit Φ(Y t−1 , Y t , X) using tree boosting A form of automatic feature discovery for CRFs19TAAI 201111/11/2011
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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: L2-Tree Boosting Algorithm Let F 0
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 and 30: Standard Approach: Log Linear (logi
Page 31 and 32: Alternating Functional Gradient Des
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
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1 - Oregon State University

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