【下载】The Elements of Statistical Learning: data mining, ...~First Edition

18 High-Dimensional Problems: p À
N 649

18.1 When p is Much Bigger than N 649

18.2 Diagonal Linear Discriminant Analysis

and Nearest Shrunken Centroids 651

18.3 Linear Classi¯ers with Quadratic Regularization 654

18.3.1 Regularized Discriminant Analysis 656

18.3.2 Logistic Regression

with Quadratic Regularization 657

18.3.3 The Support Vector Classi¯er 657

18.3.4 Feature Selection 658

18.3.5 Computational Shortcuts When p À
N 659

18.4 Linear Classi¯ers with L1 Regularization 661

18.4.1 Application of Lasso

to Protein Mass Spectroscopy 664

18.4.2 The Fused Lasso for Functional Data 666

18.5 Classi¯cation When Features are Unavailable 668

18.5.1 Example: String Kernels

and Protein Classi¯cation 668

18.5.2 Classi¯cation and Other Models Using

Inner-Product Kernels and Pairwise Distances 670

18.5.3 Example: Abstracts Classi¯cation 672

18.6 High-Dimensional Regression:

Supervised Principal Components 674

18.6.1 Connection to Latent-Variable Modeling 678

18.6.2 Relationship with Partial Least Squares 680

18.6.3 Pre-Conditioning for Feature Selection 681

18.7 Feature Assessment and the Multiple-Testing Problem 683

18.7.1 The False Discovery Rate 687

18.7.2 Asymmetric Cutpoints and the SAM Procedure 690

18.7.3 A Bayesian Interpretation of the FDR 692

18.8 Bibliographic Notes 693

Exercises 694

References 699

Author Index 729

Index 737

many thanks!!!!!!!!!!!!!!

好东西要顶起来

最近在研究 线性回归的变量选择，搜到该书，参考下。

这是第二版。