| Edition |
Second edition, corrected 7th printing. |
| Description |
1 online resource (xxii, 745 pages) : color illustrations. |
| Physical Medium |
polychrome |
| Description |
text file |
| Series |
Springer series in statistics,
0172-7397
|
|
Springer series in statistics.
0172-7397
|
| Note |
Second edition corrected at 7th printing in 2013. |
| Bibliography |
Includes bibliographical references (pages 699-727) and indexes. |
| Contents |
1. Introduction -- 2. Overview of supervised learning -- 3. Linear methods for regression -- 4. Linear methods for classification -- 5. Basis expansions and regularization -- 6. 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. |
| Summary |
"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."--Jacket. |
| Local Note |
Open Educational Resources (OER). Open Textbooks |
|
Open Textbook Library |
| Subject |
Supervised learning (Machine learning)
|
|
Supervised learning (Machine learning) |
|
Electronic data processing.
|
|
Electronic data processing. |
|
Statistics.
|
|
Statistics. |
|
Biology -- Data processing.
|
|
Biology -- Data processing. |
|
Computational biology.
|
|
Mathematics -- Data processing.
|
|
Data mining.
|
|
Computational biology. |
|
Mathematics -- Data processing. |
|
COMPUTERS -- Database Management -- Data Mining. |
|
Data mining. |
|
Supervised learning (Machine learning) |
|
Maschinelles Lernen. |
|
Statistik. |
|
Machine-learning. |
|
Datamining. |
|
Prognoses. |
|
Estatística computacional. |
|
Estatística. |
|
Mineração de dados. |
|
Inferência estatística. |
| Genre/Form |
Electronic books.
|
| Added Author |
Tibshirani, Robert, author.
|
|
Friedman, J. H. (Jerome H.), author.
|
| Other Form: |
Print version: Hastie, Trevor. Elements of statistical learning. 2nd ed. New York : Springer, ©2009 9780387848570 0387848576 (OCoLC)300478243 |
| ISBN |
9780387848587 (electronic book) |
|
0387848584 (electronic book) |
|
9781282126749 (electronic book) |
|
1282126741 (electronic book) |
|
9780387848570 (print) |
|
0387848576 (print) |
|
9780387848846 (paperback) |
|
0387848843 (paperback) |
| Standard No. |
9786612126741 |
|
