PPT-Lasso and

M agic Wand By Alex Ramirez What it is The Lasso tool allows you to draw a free form shape to create a selection T he Magic Wand tool looks for differences in color and contrast pixel differences depending upon various parameters you set. GestureClass ContextGestureTerminal Example Mnemonicick ick( )letter savesthelePunctuated: self-contained lasso( ),scribble( ),orcrop( )taporpause deletesinkunderitmnemonic lasso( )letterorscribble J. Friedman, T. Hastie, R. . Tibshirani. Biostatistics, 2008. Presented by . Minhua. Chen. 1. Motivation. Mathematical Model. Mathematical Tools. Graphical LASSO. Related papers. 2. Outline. Motivation. APRIL 14. Lasso. Smoothing Parameter Selection. Splines. Lasso – R package. l1ce() in library(“lasso2”) or lars() in library(“lars”). l1ce( y ~ . , data = dataset, bound = shrinkage.factor). April 9. Polynomial regression. Ridge regression. Lasso. Polynomial regression. lm( y ~ poly(x, degree . = . d. ). , data. =dataset). Find the optimal degree . Check the residual plots. Training and test set. : . Nisim. . Mery. . M.A. Seminar – Shrinkage Methods. Talk Agenda. Introduction - The Bias-Variance Tradeoff. The problem. Possible solutions. Discrete methods (Subset Selection). s. tructured signals: . Precise performance analysis. Christos Thrampoulidis. Joint . ITA Workshop, La Jolla, CA. February 3, 2016. Let’s start “simple”…. Given . y . and . A. can you find . Gonzalo . Mateos. , Juan A. Bazerque, and . Georgios. B. Giannakis . Acknowledgement:. . NSF grants . CCF-0830480, 1016605 and . ECCS-0824007. January 6, 2011. Distributed sparse estimation. 2. Data acquired by . Statistics for High-Dimensional Data (. Buhlmann. & van de Geer). Lasso. Proposed by . Tibshirani. (1996). Least Absolute Shrinkage and Selection Operator. Why we still use it. Accurate in prediction and variable selection (under certain assumptions) and computationally feasible. NCSU Statistical Learning Group. Will Burton. Oct. 3 2014. . The goal of regularization is to minimize some loss function (commonly sum of squared errors) while preventing. -. Overfitting. (high variance, low bias) the model on the training data set.. What it is?. The Lasso tool allows you to draw a free form shape to create a selection. .. T. he . Magic Wand . tool looks . for differences in color and contrast (pixel differences) depending upon various parameters you set.. NCSU Statistical Learning Group. Will Burton. Oct. 3 2014. . The goal of regularization is to minimize some loss function (commonly sum of squared errors) while preventing. -. Overfitting. (high variance, low bias) the model on the training data set.. J. Friedman, T. Hastie, R. . Tibshirani. Biostatistics, 2008. Presented by . Minhua. Chen. 1. Motivation. Mathematical Model. Mathematical Tools. Graphical LASSO. Related papers. 2. Outline. Motivation. Routine, Symbiotic Large-Eddy Simulation and Observation Data Bundles of Continental Shallow Convection for Improving Atmospheric Models. Core LASSO Team. PNNL: William Gustafson (PI), . Heng. Xiao. 2. R. eligious Holidays: please contact if this affects your HW due dates.. For 209 students: . please submit 209 HW separately from 109 HW in different assignments on Canvas.. A-sec this week: optional to cover 2.