Overview
- The many topics include neural networks, support vector machines, classification trees and boosting - the first comprehensive treatment of this topic in any book
- Includes more than 200 pages of four-color graphics
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Series in Statistics (SSS)
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About this book
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing 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 colour 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 factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.
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Keywords
Table of contents (18 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the authors
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.
Bibliographic Information
Book Title: The Elements of Statistical Learning
Book Subtitle: Data Mining, Inference, and Prediction, Second Edition
Authors: Trevor Hastie, Robert Tibshirani, Jerome Friedman
Series Title: Springer Series in Statistics
DOI: https://doi.org/10.1007/978-0-387-84858-7
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Science+Business Media, LLC, part of Springer Nature 2009
Hardcover ISBN: 978-0-387-84857-0Published: 09 February 2009
eBook ISBN: 978-0-387-84858-7Published: 26 August 2009
Series ISSN: 0172-7397
Series E-ISSN: 2197-568X
Edition Number: 2
Number of Pages: XXII, 745
Number of Illustrations: 54 b/w illustrations, 604 illustrations in colour
Topics: Artificial Intelligence, Data Mining and Knowledge Discovery, Probability Theory and Stochastic Processes, Statistical Theory and Methods, Computational Biology/Bioinformatics, Computer Appl. in Life Sciences