The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second EditionDuring 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. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting. |
From inside the book
Results 1-5 of 60
... nonlinear dimension reduction, Google page rank algorithm, a direct approach to ICA 15. Random Forests New 16. Ensemble Learning New 17. Undirected Graphical Models New 18. High-Dimensional Problems New Some further notes: • Our first ...
... Nonlinear Dimension Reduction and Local Multidimensional Scaling . . . . . . . . . . . . 572 14.10 The Google PageRank Algorithm . . . . . . . . . . . . . 576 Bibliographic Notes . . . . . . . . . . . . . . . . . . . . . . . . . 578 ...
... nonlinear and disjoint, and as such will be much more difficult to obtain. We now look at another classification and regression procedure that is in some sense at the opposite end of the spectrum to the linear model, and far better ...
... nonlinear expansions, might be such x21, as x 1 the x22, cos(x1) sigmoid and so on. We transformation common to neural network models, 1 hk (x) = 1 + exp(-xTβ k) . (2.31) We can use least squares to estimate the parameters θ in fθ as we ...
... nonlinear problem. In practice, shortcuts such as greedy algorithms or two stage processes are used. Section 6.7 describes some such approaches. A single-layer feed-forward neural network model with linear output weights can be thought ...
Contents
1 | |
9 | |
43 | |
4 Linear Methods for Classification | 100 |
5 Basis Expansions and Regularization | 139 |
6 Kernel Smoothing Methods | 190 |
7 Model Assessment and Selection | 219 |
8 Model Inference and Averaging | 261 |
12 Support Vector Machines and Flexible Discriminants | 417 |
13 Prototype Methods and NearestNeighbors | 459 |
14 Unsupervised Learning | 485 |
15 Random Forests | 586 |
16 Ensemble Learning | 605 |
17 Undirected Graphical Models | 625 |
p N | 649 |
References | 699 |
9 Additive Models Trees and Related Methods | 295 |
10 Boosting and Additive Trees | 337 |
11 Neural Networks | 388 |
Author Index | 729 |
Index | 737 |