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 94
... Parameters 5.5.1 Fixing the Degrees of Freedom 5.5.2 5.6 Nonparametric Logistic Regression 5.7 Multidimensional Splines 127 129 134 . 134 · 134 137 138 Examples of RKHS 5.8 Regularization and Reproducing Kernel Hilbert Spaces 5.8.1 ...
... Parameters 7.7 The Bayesian Approach and BIC 7.8 Minimum Description Length 7.9 Vapnik - Chernovenkis Dimension 7.9.1 Example ( Continued ) 7.10 Cross - Validation 7.11 Bootstrap Methods 7.11.1 Example ( Continued ) Bibliographic Notes ...
... parameters , and hence its minimum always exists , but may not be unique . The solution is easiest to characterize in matrix notation . We can write RSS ( B ) = ( y - Xẞ ) T ( y - Xẞ ) , ( 2.4 ) where X is an N × p matrix with each row ...
... parameter , the num- ber of neighbors k , compared to the p parameters in least - squares fits . Al- though this is the case , we will see that the effective number of parameters of k - nearest neighbors is N / k and is generally bigger ...
... parameters that can be modified to suit the data at hand . For example , the linear model f ( x ) = xTẞ has = B. Another class of useful approxi- xaß ◊ mators can be expressed as linear basis expansions K fo ( x ) = Σhk ( x ) 0k , k ...
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 |