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 83
... observations in the training set T clos- est in input space to x to form Ŷ . Specifically , the k - nearest neighbor fit for Ŷ is defined as follows : 1 Ý ( 2 ) ― k Σ Yi , XiЄNk ( x ) ( 2.8 ) where Nk ( x ) is the neighborhood of a ...
... observations as follows : for each observation , we picked an mk at random with probability 1/10 , and then generated a N ( mk , I / 5 ) , thus leading to a mixture of Gaussian clusters for each class . Figure 2.4 shows the results of ...
... observations close to any x and average them . This approach and our intuition breaks down in high dimensions , and the phenomenon is commonly referred to as the curse of dimensionality ( Bellman , 1961 ) . There are many manifestations ...
... observations we average , the higher is the variance of our fit . Another consequence of the sparse sampling in high dimensions is that all sample points are close to an edge of the sample . Consider N data points uniformly distributed ...
... observations with tied or identical values of x , then the fit can be obtained from a reduced weighted least squares problem . Ex . 2.6 Suppose we have a sample of N pairs x¿ , y ; drawn i.i.d. from the distribution characterized as ...
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 |