PPT-Resampling MEthods Dr. Pei Xu

Auburn University Wednesday September 13 2017 IOM 530 Intro to Statistical Learning 1 Outline Cross Validation The Validation Set Approach LeaveOneOut Cross Validation Kfold Cross Validation. Within these topics it was important that students were adept at algebraic manipul ation and arithmetic computation operations with integers decimals and fractions Algebraic manipulation was prominent in Questions 3 5a 5b 6 7a 8 9a 9ci 10a and They are motivated by the dependence of the Taylor methods on the speci64257c IVP These new methods do not require derivatives of the righthand side function in the code and are therefore generalpurpose initial value problem solvers RungeKutta metho example Consider a short data set data 1 2 3 4 5 6 7 8 9 10 ans 1 2 3 4 5 6 7 8 9 10 excludedata5 ans 1 2 3 4 6 7 8 9 10 excludedata2 5 10 ans 1 3 4 6 7 8 9 If data is a matrix exclude works by rows x 1 2 3 4 5 6 7 8 ans 1 2 3 4 5 6 7 8 excludex2 resampling of the image information in regions far from the stitching boundaries, in order to deliver a continuous-appearing composite image. 5.Merging (combining) of the image (pixel-value) data at a Bayes rule. Popular classification methods. Logistic regression . Linear discriminant analysis (LDA)/QDA and Fisher criteria. K-nearest neighbor (KNN). Classification and regression tree (CART). Bagging. Wilcoxon Rank-Sum Test . To compare two independent samples. Null is that the two populations are identical. The test statistic is . W. s. . , Table of Critical . Vals. .. For large samples, there is a normal approx.. Inadditiontothechallengesthatwehaedescribed,thefeaturevectormustbeabletosurviveimageformatersion(e.g.,fromJPEGtoGifandviceversa),resampling,andrequantization.Furthermore,RIMEmustalsobeabletocopewithge . A New Paradigm of Using Workload Data for Performance Evaluation. Dror . Feitelson. Hebrew University. Performance Evaluation. “Experimental computer science at its best” [Denning]. Major element of systems research. (Part 1). Allan Rossman, Cal Poly – San Luis Obispo. Robin Lock, St. Lawrence University. George Cobb (. TISE. , 2007). 2. “What . we teach is largely the technical machinery of numerical approximations based on the normal distribution and its many subsidiary cogs. This machinery was once necessary, because the conceptually simpler alternative based on permutations was computationally beyond our . Google Earth. Geometric Corrections. Rectification and Registration. Learning Objectives. Be able to define geometric correction.. Understand why geometric correction is usually necessary.. Understand the difference between . in Seismic Reservoir Modeling. Cheolkyun Jeong*, Tapan Mukerji, and Gregoire Mariethoz. Stanford Center for Reservoir Forecasting. How to quantify uncertainty of models? . Why quantify uncertainty?. 2. Today’s agenda:. Discuss the purpose and structure of the Materials and Methods section.. Examine the Materials and Methods sections of the papers that students chose. How are they similar and different? What works and what does not? . Methods that return a value. Void . methods. Programmer defined methods. Scope. Top Down Design. Objectives. At the end of this topic, students should be able to:. Write programs that use built-in methods. Instead of optimizing a single design point, population methods optimize a collection of . individuals. A large number of individuals prevents algorithm from being stuck in a local minimum. Useful information can be shared between individuals.