The elements of statistical learning : data mining, inference and prediction
Trevor Hastie (Author), Robert Tibshirani (Author), Jerome Friedman (Author)
"During 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 and 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."--Publisher's website
Springer eBooks
Statistics
1 online resource (xxii, 745 pages) : illustrations
9780387848570, 9780387848587, 9781282827264, 9781282126749, 9786612126741, 0387848576, 0387848584, 128282726X, 1282126741, 6612126744
1058138445
1. Introduction
2. Overview of supervised learning
3. Linear methods for regression
4. Linear methods for classification
5. Basis expansions and regularization
Kernel smoothing methods
7. Model assessment and selection
8. Model inference and averaging
9. Additive models, trees and related methods
10. Boosting and additive trees
11. Neural networks
12. Support vector machines and flexible discriminants
13. Prototype methods and nearest-neighbors
14. Unsupervised learning
15. Random forests
16. Ensemble learning
17. Undirected graphical models
18. High-dimensional problems: p>> N
English