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《The Elements of Statistical Learning Data Mining Inference and Prediction Second Edition》Trevor Hastie
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This is page viiPrinter:Opaque thisPreface to the Second EditionIn God we trust,all others bring data.-William Edwards Deming (1900-1993)We have been gratified by the popularity of the first edition of TheElements of Statistical Learning.This,along with the fast pace of researchin the statistical learning field,motivated us to update our book with asecond edition.We have added four new chapters and updated some of the existingchapters.Because ny readers are familiar with the layout of the firstedition,we have tried to change it as little as possible.Here is a sumryof the in changes:IOn the Web,this quote has been widely attributed to both Deming and Robert W.Hayden;however Profesor Hayden told us that he can claim no credit for this quote,and ironically we could find no "data"confirming that Deming actually said this.Preface to the Second EditionChapterWhat's new1.Introduction2.Overview of Supervised Learning3.Linear Methods for RegressionLAR algorithm and generalizationsof the lasso4.Linear Methods for ClassificationLasso path for logistic regression5.Basis Expansions and Regulariza-Additional illustrations of RKHStion6.Kernel Smoothing Methods7.Model Assesent and SelectionStrengths and pitfalls of cross-validation8.Model Inference and Averaging9.Additive Models,Trees,andRelated Methods10.Boosting and Additive TreesNew example from ecology;someterial split off to Chapter 16.11.Neural NetworksBayesian neural nets and the NIPS2003 challenge12.Support Vector Machines andPath algorithm for SVM classifierFlexible Discriminants13.Prototype Methods andNearest-Neighbors14.Unsupervised LearningSpectral clustering,kernel PCA,sparse PCA,non-negative trixfactorization archetypal ysis,nonlinear dimension reduction,Google page rank algorithm,adirect approach to ICA15.Random ForestsNew16.Ensemble LearningNew17.Undirected Graphical ModelsNew18.High-Dimensional ProblemsNewSome further notes:Our first edition was unfriendly to colorblind readers;in particular,we tended to favor red/green contrasts which are particularly trou-blesome.We have changed the color palette in this edition to a largeextent,replacing the above with an orange/blue contrast.·We have changed the name of Chapter6from“Kernel Methods'”to"Kernel Smoothing Methods",to avoid confusion with the chine-learning kernel method that is discussed in the context of support vec-tor chines (Chapter 11)and more generally in Chapters 5 and 14..In the first edition,the discussion of error-rate estition in Chap-ter 7 was sloppy,as we did not clearly differentiate the notions ofconditional error rates (conditional on the training set)and uncondi-tional rates.We have fixed this in the new edition.
