The Elements of Statistical Learning
Data Mining, Inference, and Prediction, Second Edition
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…
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Produktdetails
Weitere Autoren: Tibshirani, Robert / Friedman, Jerome
- ISBN: 978-0-387-84857-0
- EAN: 9780387848570
- Produktnummer: 4290300
- Verlag: Springer Nature EN
- Sprache: Englisch
- Erscheinungsjahr: 2017
- Seitenangabe: 745 S.
- Masse: H24.4 cm x B16.9 cm x D4.0 cm 1'208 g
- Auflage: 2. A.
- Abbildungen: s/w. Abb.
- Gewicht: 1208
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.
9 weitere Werke von Trevor Hastie:
An Introduction to Statistical Learning
Data Mining, Inference, and Prediction, Second Edition
Data Mining, Inference, and Prediction, Second Edition
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The Elements of Statistical Learning
Data Mining, Inference, and Prediction, Second Edition
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An Introduction to Statistical Learning
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Statistical Learning with Sparsity
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