| 查看: 4279 | 回复: 84 | ||||||||
| 【奖励】 本帖被评价77次，作者jlcuit增加金币 59.6 个 | ||||||||
[资源]
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
|
||||||||
|
最近在看数据统计方面的资料，查到一本书，分享给大家，希望有帮助~ The Elements of Statistical Learning: Data Mining, Inference, and Prediction Second Edition Trevor Hastie Robert Tibshirani Jerome Friedman Springer，2008 内容简介 During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for "wide" data (p bigger than n), including multiple testing and false discovery rates. 目录 Preface to the Second Edition vii Preface to the First Edition xi 1 Introduction 1 2 Overview of Supervised Learning 9 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Variable Types and Terminology . . . . . . . . . . . . . . 9 2.3 Two Simple Approaches to Prediction: Least Squares and Nearest Neighbors . . . . . . . . . . . 11 2.3.1 Linear Models and Least Squares . . . . . . . . 11 2.3.2 Nearest-Neighbor Methods . . . . . . . . . . . . 14 2.3.3 From Least Squares to Nearest Neighbors . . . . 16 2.4 Statistical Decision Theory . . . . . . . . . . . . . . . . . 18 2.5 Local Methods in High Dimensions . . . . . . . . . . . . . 22 2.6 Statistical Models, Supervised Learning and Function Approximation . . . . . . . . . . . . . . . . 28 2.6.1 A Statistical Model for the Joint Distribution Pr(X,Y ) . . . . . . . 28 2.6.2 Supervised Learning . . . . . . . . . . . . . . . . 29 2.6.3 Function Approximation . . . . . . . . . . . . . 29 2.7 Structured Regression Models . . . . . . . . . . . . . . . 32 2.7.1 Difficulty of the Problem . . . . . . . . . . . . . 32 xiv Contents 2.8 Classes of Restricted Estimators . . . . . . . . . . . . . . 33 2.8.1 Roughness Penalty and Bayesian Methods . . . 34 2.8.2 Kernel Methods and Local Regression . . . . . . 34 2.8.3 Basis Functions and Dictionary Methods . . . . 35 2.9 Model Selection and the Bias–Variance Tradeoff . . . . . 37 Bibliographic Notes . . . . . . . . . . . . . . . . . . . . . . . . . 39 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3 Linear Methods for Regression 43 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.2 Linear Regression Models and Least Squares . . . . . . . 44 3.2.1 Example: Prostate Cancer . . . . . . . . . . . . 49 3.2.2 The Gauss–Markov Theorem . . . . . . . . . . . 51 3.2.3 Multiple Regression from Simple Univariate Regression . . . . . . . . 52 3.2.4 Multiple Outputs . . . . . . . . . . . . . . . . . 56 3.3 Subset Selection . . . . . . . . . . . . . . . . . . . . . . . 57 3.3.1 Best-Subset Selection . . . . . . . . . . . . . . . 57 3.3.2 Forward- and Backward-Stepwise Selection . . . 58 3.3.3 Forward-Stagewise Regression . . . . . . . . . . 60 3.3.4 Prostate Cancer Data Example (Continued) . . 61 3.4 Shrinkage Methods . . . . . . . . . . . . . . . . . . . . . . 61 3.4.1 Ridge Regression . . . . . . . . . . . . . . . . . 61 3.4.2 The Lasso . . . . . . . . . . . . . . . . . . . . . 68 3.4.3 Discussion: Subset Selection, Ridge Regression and the Lasso . . . . . . . . . . . . . . . . . . . 69 3.4.4 Least Angle Regression . . . . . . . . . . . . . . 73 3.5 Methods Using Derived Input Directions . . . . . . . . . 79 3.5.1 Principal Components Regression . . . . . . . . 79 3.5.2 Partial Least Squares . . . . . . . . . . . . . . . 80 3.6 Discussion: A Comparison of the Selection and Shrinkage Methods . . . . . . . . . . . . . . . . . . . 82 3.7 Multiple Outcome Shrinkage and Selection . . . . . . . . 84 3.8 More on the Lasso and Related Path Algorithms . . . . . 86 3.8.1 Incremental Forward Stagewise Regression . . . 86 3.8.2 Piecewise-Linear Path Algorithms . . . . . . . . 89 3.8.3 The Dantzig Selector . . . . . . . . . . . . . . . 89 3.8.4 The Grouped Lasso . . . . . . . . . . . . . . . . 90 3.8.5 Further Properties of the Lasso . . . . . . . . . . 91 3.8.6 Pathwise Coordinate Optimization . . . . . . . . 92 3.9 Computational Considerations . . . . . . . . . . . . . . . 93 Bibliographic Notes . . . . . . . . . . . . . . . . . . . . . . . . . 94 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 Contents xv 4 Linear Methods for Classification 101 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 101 4.2 Linear Regression of an Indicator Matrix . . . . . . . . . 103 4.3 Linear Discriminant Analysis . . . . . . . . . . . . . . . . 106 4.3.1 Regularized Discriminant Analysis . . . . . . . . 112 4.3.2 Computations for LDA . . . . . . . . . . . . . . 113 4.3.3 Reduced-Rank Linear Discriminant Analysis . . 113 4.4 Logistic Regression . . . . . . . . . . . . . . . . . . . . . . 119 4.4.1 Fitting Logistic Regression Models . . . . . . . . 120 4.4.2 Example: South African Heart Disease . . . . . 122 4.4.3 Quadratic Approximations and Inference . . . . 124 4.4.4 L1 Regularized Logistic Regression . . . . . . . . 125 4.4.5 Logistic Regression or LDA? . . . . . . . . . . . 127 4.5 Separating Hyperplanes . . . . . . . . . . . . . . . . . . . 129 4.5.1 Rosenblatt’s Perceptron Learning Algorithm . . 130 4.5.2 Optimal Separating Hyperplanes . . . . . . . . . 132 Bibliographic Notes . . . . . . . . . . . . . . . . . . . . . . . . . 135 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 5 Basis Expansions and Regularization 139 6 Kernel Smoothing Methods 191 7 Model Assessment and Selection 219 8 Model Inference and Averaging 261 9 Additive Models, Trees, and Related Methods 295 10 Boosting and Additive Trees 337 11 Neural Networks 389 12 Support Vector Machines and Flexible Discriminants 417 13 Prototype Methods and Nearest-Neighbors 459 14 Unsupervised Learning 485 15 Random Forests 587 16 Ensemble Learning 605 17 Undirected Graphical Models 625 18 High-Dimensional Problems: p ≫ N 649 |
回复此楼» 本帖附件资源列表
-
欢迎监督和反馈：小木虫仅提供交流平台，不对该内容负责。
本内容由用户自主发布，如果其内容涉及到知识产权问题，其责任在于用户本人，如对版权有异议，请联系邮箱：xiaomuchong@tal.com - 附件 1 : The_Elements_of_Statistical_Learning_Data_Mining,_Inference,_and_Prediction.pdf
2015-05-09 21:06:40, 12.16 M
» 收录本帖的淘帖专辑推荐
 遥感图像处理专辑 | 科研与育人 | 专业书籍 | 李的收藏 |
| @数学参考资料 |
» 猜你喜欢
地质学复试调剂
已经有1人回复
-
诚邀
已经有0人回复
-
地球化学论文润色/翻译怎么收费?
已经有171人回复
-
考研求调剂
已经有4人回复
-
考研求调剂
已经有2人回复
-
东北石油大学地球科学学院地质资源与地质工程专业接收调剂
已经有0人回复
-
明年散金币
已经有1人回复
» 本主题相关价值贴推荐，对您同样有帮助:
- 剑桥2012年Modern.Statistical.Methods.for.Astronomy.With.R.Applications
已经有31人回复
- 数据挖掘经典书《DataMining Practical Machine Learning Tools and Techniques》
已经有293人回复
- 经典书籍<Statistics for Spatial Data>
已经有19人回复
- 【分享】概率论沉思录--清晰版PDF.pdf
已经有146人回复
- 【讨论】机器学习理论基础
已经有33人回复
6楼2015-05-12 06:24:26
22楼2015-05-14 08:41:37
35楼2015-05-31 22:23:38
简单回复
dengxg682楼
2015-05-09 21:51
回复
五星好评 顶一下，感谢分享！
nono20093楼
2015-05-10 07:55
回复
五星好评 顶一下，感谢分享！
2015-05-10 22:49
回复
五星好评 顶一下，感谢分享！
xsc43215楼
2015-05-11 18:57
回复
五星好评 顶一下，感谢分享！
2015-05-12 06:36
回复
五星好评 顶一下，感谢分享！
supervb8楼
2015-05-12 08:31
回复
五星好评 顶一下，感谢分享！
2015-05-12 08:50
回复
五星好评 顶一下，感谢分享！
ykyang10楼
2015-05-12 08:53
回复
五星好评 顶一下，感谢分享！
wyf_199911楼
2015-05-12 15:07
回复
五星好评 顶一下，感谢分享！
zsma788012楼
2015-05-12 15:49
回复
五星好评 顶一下，感谢分享！
daijzh13楼
2015-05-12 18:11
回复
五星好评 顶一下，感谢分享！
yumoym14楼
2015-05-12 22:44
回复
一般 顶一下，感谢分享！
parkzhu15楼
2015-05-13 06:31
回复
五星好评 顶一下，感谢分享!
parkzhu16楼
2015-05-13 06:33
回复
顶一下，感谢分享！
lieying1817楼
2015-05-13 09:49
回复
五星好评 顶一下，感谢分享！
hms200618楼
2015-05-13 12:33
回复
五星好评 顶一下，感谢分享！
Newsam19楼
2015-05-13 18:59
回复
五星好评 顶一下，感谢分享！
lxw20220楼
2015-05-13 23:35
回复
五星好评 顶一下，感谢分享！
xcontext21楼
2015-05-14 05:49
回复
五星好评 顶一下，谢谢分享！ [ 发自小木虫客户端 ]
tianyi42323楼
2015-05-14 10:51
回复
五星好评 顶一下，感谢分享！
不第书生24楼
2015-05-14 13:24
回复
五星好评 顶一下，感谢分享！
wenhsien25楼
2015-05-15 07:03
回复
五星好评 顶一下，感谢分享！
xshk26楼
2015-05-15 07:35
回复
五星好评 顶一下，感谢分享！
dectab27楼
2015-05-15 08:21
回复
五星好评 顶一下，感谢分享！
future4us28楼
2015-05-15 10:29
回复
五星好评 顶一下，感谢分享！
zhchzhsh207629楼
2015-05-15 11:02
回复
顶一下，感谢分享！
chemphys30楼
2015-05-15 11:12
回复
五星好评 顶一下，感谢分享！
53163043431楼
2015-05-15 14:56
回复
五星好评 顶一下，感谢分享！
cnbchen32楼
2015-05-15 18:10
回复
五星好评 顶一下，感谢分享！
wanghl531133楼
2015-05-15 19:06
回复
五星好评 顶一下，感谢分享！
linxingzhou34楼
2015-05-16 18:18
回复
五星好评 顶一下，感谢分享！
konexgsp36楼
2015-06-01 11:01
回复
五星好评 顶一下，感谢分享！
ayan9937楼
2015-06-02 01:06
回复
三星好评 顶一下，感谢分享！
anandaho38楼
2015-06-27 22:53
回复
五星好评 顶一下，感谢分享！
bonjour_uk39楼
2015-07-03 15:10
回复
一般 顶一下，感谢分享！
lazebone40楼
2015-07-07 16:58
回复
五星好评 顶一下，感谢分享！
rebornxldeng41楼
2015-07-28 16:56
回复
五星好评 顶一下，感谢分享！
bullmanwyz42楼
2015-09-01 16:47
回复
五星好评 顶一下，感谢分享！
zhourumin43楼
2015-09-10 12:07
回复
五星好评 顶一下，感谢分享！
zeng_201444楼
2015-09-19 17:53
回复
五星好评 顶一下，感谢分享！
Scottnong45楼
2015-11-21 11:23
回复
五星好评 顶一下，感谢分享！
slli46楼
2015-11-26 17:41
回复
五星好评 顶一下，感谢分享！
boboskym47楼
2015-12-03 16:02
回复
五星好评 顶一下，感谢分享！
ggsdl48楼
2015-12-17 11:01
回复
五星好评 顶一下，感谢分享！
yongganll49楼
2016-01-02 17:24
回复
五星好评 顶一下，感谢分享！
bwbjlt50楼
2016-01-04 08:54
回复
五星好评 顶一下，感谢分享！
投票:
10