| 查看: 4032 | 回复: 84 | |||||||
| 【奖励】 本帖被评价77次，作者jlcuit增加金币 59.6 个 | |||||||
| 当前只显示满足指定条件的回帖，点击这里查看本话题的所有回帖 | |||||||
[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
青C最后一年，散金祈福吧
已经有48人回复
-
定了定了，明天出结果！2025 国自然基金结果即将揭晓，查询攻略看这里
已经有107人回复
-
地球物理学和空间物理学论文润色/翻译怎么收费?
已经有115人回复
-
2025国家自然科学基金放榜时间，根据官方回应，8月15日基本没戏了
已经有5人回复
-
没中，有点难受，科研也许就到这了，回归生活吧
已经有15人回复
-
地区已中，感谢虫友们！
已经有109人回复
-
各位未来院士帮忙看看评审意见
已经有9人回复
-
《风吹过日常的缝隙》
已经有0人回复
-
355 B区地质调剂
已经有1人回复
-
解锁青少年负责任决策的关键：基于PISA 2022全球数据的SEL维度研究成果发表
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
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！
