1 - Oregon State University

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accuracyFlexible Machine Learning Models Support Vector Machines Classification andRegression Trees Key advantage: Can tunethe complexity of themodel to the complexityof the data Structural Risk Minimizationcomplexity8TAAI 201111/11/2011
Another Advantage:Interactions and NonlinearitiesSVMs:Polynomial kernels captureinteractions and polynomialnonlinearitiesGaussian kernels capturenonlinearities, however, interactionsare embedded in the distancefunction (typically Euclidean)Classification and regression treesInteractions are captured by the ifthen-elsestructure of the treeNonlinearities are approximated bypiecewise constant functionsY 1 =−5X 1 ≥ 3X 2 ≥ 0 X 2 ≥ 0Y 1 =3Y 1 =8Y 1 =1Y 1 = −5 ⋅ I X 1 ≥ 3, X 2 ≥ 0 + 3 ⋅ I X 1 ≥ 3, X 2 < 0 +8 ⋅ I x 1 < 3, X 2 ≥ 0 + 1 ⋅ I(X 1 < 3, X 2 < 0)9TAAI 201111/11/2011
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: Advantages and Disadvantages ofPara
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 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
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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