The elements of statistical learning : data mining, inference, and prediction /
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. I...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , |
| Format: | Textbook |
| Language: | English Vietnamese |
| Published: |
New York, NY :
Springer,
2017.
|
| Edition: | 2nd ed. |
| Series: | Springer series in statistics,
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| LEADER | 02274cam a22003375a 4500 | ||
|---|---|---|---|
| 005 | 20210311165647.0 | ||
| 008 | 210311s2017 nyua b 001 0 eng | ||
| 999 | |c 6450 |d 6450 | ||
| 020 | |a 9780387848570 |c 3225000đ | ||
| 040 | |a Phenikaa Uni |b vie |e aacr2 |c Phenikaa Uni | ||
| 041 | |a vie | ||
| 044 | |a vm | ||
| 082 | 0 | 0 | |a 006.3 |2 23 |b TH200E 2017 |
| 100 | 1 | |a Hastie, Trevor. | |
| 245 | 1 | 4 | |a The elements of statistical learning : |b data mining, inference, and prediction / |c Trevor Hastie, Robert Tibshirani, Jerome Friedman. |
| 250 | |a 2nd ed. | ||
| 260 | |a New York, NY : |b Springer, |c 2017. | ||
| 300 | |a xxii, 745 p. : |b ill. (some col.) ; |c 25 cm. | ||
| 490 | 0 | |a Springer series in statistics, |x 0172-7397 | |
| 520 | 3 | |a 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 any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Statistics |x Methodology. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Bioinformatics. | |
| 650 | 0 | |a Inference. | |
| 650 | 0 | |a Forecasting. | |
| 650 | 0 | |a Computational intelligence. | |
| 700 | 1 | |a Tibshirani, Robert. | |
| 700 | 1 | |a Friedman, J. H. |q (Jerome H.) | |
| 942 | |2 ddc |c SGT | ||
| 952 | |0 0 |1 0 |2 ddc |4 0 |6 006_300000000000000_TH200E_2017 |7 1 |9 26294 |a PHENIKAA |b PHENIKAA |c PNK_103 |d 2021-03-11 |g 3235000.00 |l 0 |o 006.3 TH200E 2017 |p 00026451 |r 2021-03-11 |v 3235000.00 |w 2021-03-11 |y SGT | ||