The Elements of Statistical Learning: Data Mining, Inference, and PredictionDuring 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 89
... Procedure 8.6 MCMC for Sampling from the Posterior 8.7 Bagging • 8.7.1 Example : Trees with Simulated Data . 8.8 Model Averaging and Stacking 8.9 Stochastic Search : Bumping Bibliographic Notes Exercises • • • 9 Additive Models , Trees ...
... procedure for envelopes . This is a classification problem for which the error rate needs to be kept very low to avoid misdirection of mail . In order to achieve this low error rate , some objects can be assigned to a " don't know ...
... procedure called boosting is the focus of Chapter 10 . In Chapters 9-13 we describe a series of structured methods for super- vised learning , with Chapters 9 and 11 covering regression and Chapters 12 and 13 focussing on classification ...
... procedure that is in some sense at the opposite end of the spectrum to the linear model , and far better suited to the second scenario . 2.3.2 Nearest - Neighbor Methods Nearest - neighbor methods use those observations in the training ...
... procedure , followed by classification to the largest fitted value , is another way of representing the Bayes classifier . Although this theory is exact , in practice problems can occur , depending on the regression model used . For ...
Contents
1 | |
3 | |
5 | |
7 | |
9 | |
11 | |
Bibliographic Notes | 75 |
41 | 108 |
79 | 282 |
Bibliographic Notes | 295 |
Support Vector Machines | 350 |
Bibliographic Notes | 367 |
Flexible Discriminants | 371 |
Bibliographic Notes | 406 |
Prototype Methods and NearestNeighbors | 410 |
Unsupervised Learning | 437 |
55 | 146 |
Bibliographic Notes | 155 |
73 | 159 |
Kernel Methods | 165 |
Additive Models Trees and Related Methods | 257 |
165 | 264 |
Bibliographic Notes | 504 |
81 | 511 |
91 | 517 |
Author Index | 523 |
95 | 530 |