The Elements of Statistical Learning
Data Mining, Inference, and Prediction, Second Edition
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 ra…
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Produktdetails
Weitere Autoren: Tibshirani, Robert / Friedman, Jerome
- ISBN: 978-0-387-84858-7
- EAN: 9780387848587
- Produktnummer: 33420530
- Verlag: Springer-Verlag GmbH
- Sprache: Englisch
- Erscheinungsjahr: 2009
- Seitenangabe: 745 S.
- Plattform: PDF
- Masse: 17'386 KB
- Auflage: 2nd ed. 2009
- Abbildungen: 658 schwarz-weiße Abbildungen, Bibliographie
Über den Autor
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.
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