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Genre/Form: | Statistics Electronic books manuel |
---|---|
Additional Physical Format: | Print version: Hastie, Trevor. Elements of statistical learning. New York : Springer, ©2009 (OCoLC)300478243 |
Material Type: | Document, Internet resource |
Document Type: | Internet Resource, Computer File |
All Authors / Contributors: |
Trevor Hastie; Robert Tibshirani; Jerome Friedman |
ISBN: | 9780387848570 0387848576 9780387848587 0387848584 9781282827264 128282726X 1282126741 9781282126749 9786612126741 6612126744 |
OCLC Number: | 1058138445 |
Language Note: | English. |
Description: | 1 online resource (xxii, 745 pages) : illustrations |
Contents: | 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. |
Series Title: | Springer series in statistics. |
Responsibility: | Trevor Hastie, Robert Tibshirani, Jerome Friedman. |
Abstract:
"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.
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Related Subjects:(27)
- Machine learning.
- Statistics -- Methodology.
- Bioinformatics.
- Computational intelligence.
- Inference.
- Forecasting.
- Computational biology.
- Electronic data processing.
- Mathematics -- Data processing.
- Statistics.
- Biology -- Data processing.
- Data mining.
- Supervised learning (Machine learning)
- Data Mining.
- Statistics as Topic.
- Computational Biology.
- Mathematical Computing.
- Apprentissage supervisé (Intelligence artificielle)
- Maschinelles Lernen
- Statistik
- Estatística computacional.
- Estatística.
- Mineração de dados.
- Inferência estatística.
- exploration de données -- manuel.
- inférence statistique -- manuel.
- prévision statistique -- manuel.