The elements of statistical learning: data mining, inference, and prediction
(Book)
Author:
Contributors:
Published:
New York : Springer, [2009].
Format:
Book
Edition:
2nd ed.
Physical Desc:
xxii, 745 pages : illustrations (some color) ; 24 cm.
Status:
Copies
Location
Call Number
Status
Last Check-In
ASU Main (3rd floor)
Q325.75 .H37 2009
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Citations
APA Citation (style guide)
Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York, Springer.
Chicago / Turabian - Author Date Citation (style guide)Hastie, Trevor, Robert, Tibshirani and J. H. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, Springer.
Chicago / Turabian - Humanities Citation (style guide)Hastie, Trevor, Robert, Tibshirani and J. H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, Springer, 2009.
MLA Citation (style guide)Hastie, Trevor,, et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York, Springer, 2009.
Note! Citation formats are based on standards as of July 2022. Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy.
Description
"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.
More Details
Language:
English
ISBN:
9780387848570, 0387848576
Notes
Bibliography
Includes bibliographical references (pages [699]-727) and indexes.
Description
"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.
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Grouped Work ID:
0ff0366d-7ae2-b59b-6239-3f073aa58465
Record Information
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| 250 | |a 2nd ed. | ||
| 264 | 1 | |a New York : |b Springer, |c [2009] | |
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| 504 | |a Includes bibliographical references (pages [699]-727) and indexes. | ||
| 505 | 0 | 0 | |g 1. |t Introduction -- |g 2. |t Overview of supervised learning -- |g 3. |t Linear methods for regression -- |g 4. |t Linear methods for classification -- |g 5. |t Basis expansions and regularization -- |g 6. |t Kernel smoothing methods -- |g 7. |t Model assessment and selection -- |g 8. |t Model inference and averaging -- |g 9. |t Additive models, trees, and related methods -- |g 10. |t Boosting and additive trees -- |g 11. |t Neural networks -- |g 12. |t Support vector machines and flexible discriminants -- |g 13. |t Prototype methods and nearest-neighbors -- |g 14. |t Unsupervised learning -- |g 15. |t Random forests -- |g 16. |t Ensemble learning -- |g 17. |t Undirected graphical models -- |g 18. |t High-dimensional problems: p>> N. |
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