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 57
... tion model , or learner , which will enable us to predict the outcome for new unseen objects . A good learner is one that accurately predicts such an outcome . The examples above describe what is called the supervised learning prob- lem ...
... tion rule . 2.3 Two Simple Approaches to Prediction : Least Squares and Nearest Neighbors In this section we develop two simple but powerful prediction methods : the linear model fit by least squares and the k - nearest - neighbor ...
... tion is that one assumes that the regression function f ( x ) is approximately linear in its arguments : f ( x ) ≈ x1ß . ( 2.15 ) This is a model - based approach — we specify a model for the regression func- tion . Plugging this ...
... tion . Then ƒ ( X ) = E ( Y | X ) = Pr ( G = G1 | X ) if G1 corresponded to Y = 1 . Likewise for a K - class problem , E ( Y | X ) Gk X ) . This shows that our dummy - variable regression procedure , followed by classification to the ...
... tion ( no noise ) in R : ƒ ( X ) - 8 || X || 2 and demonstrates the error that 1 - nearest neighbor makes in estimating f ( 0 ) . The training point is indicated by the blue tick mark . The top right panel illustrates why the radius of ...
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 |