MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03355nam a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MIUC |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20200219142835.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
170908s2009 nyua 001 | eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9780387848570 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
MIUC |
| Language of cataloging |
eng |
| Transcribing agency |
MIUC |
| 082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
519.5 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| 9 (RLIN) |
3158 |
| Personal name |
Hastie, Trevor |
| 952 ## - Items |
|---|
| Itemnumber |
1807 |
| 245 14 - TITLE STATEMENT |
|---|
| Title |
The elements of statistical learning : |
| Remainder of title |
data mining, inference, and prediction / |
| Statement of responsibility, etc. |
Trevor Hastie, Robert Tibshirani, Jerome Friedman. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
2nd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
New York : |
| Name of publisher, distributor, etc. |
Springer, |
| Date of publication, distribution, etc. |
2009. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
745 p. : |
| Other physical details |
ill. b&w and col. ; |
| Dimensions |
25 cm. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 490 1# - SERIES STATEMENT |
|---|
| Series statement |
Springer series in statistics, |
| International Standard Serial Number |
01727397 ; |
| Volume/sequential designation |
692 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references (p. [699]-727) and indexes. |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Ch. 1. Introduction -- <br/>Ch. 2. Overview of supervised learning -- <br/>Ch. 3. Linear method for regression -- <br/>Ch. 4. Linear methods for classification -- <br/>Ch. 5. Basis expansions and regularization -- <br/>Ch. 6 Kernel smoothing methods -- <br/>Ch. 7. Model assessment and selection -- <br/>Ch. 8. Model inference and averaging -- <br/>Ch. 9. Additive model, trees and related methods -- <br/>Ch. 10. Boosting and additive trees -- <br/>Ch. 11. Neural networks -- <br/>Ch. 12. Support vector machines and flexible discriminants -- <br/>Ch. 13. Prototype methods and nearest-neighbors -- <br/>Ch. 14. Unsupervised learning -- <br/>Ch. 15. Random forests -- <br/>Ch. 16. Ensemble learning -- <br/>Ch. 17. Undirected graphical models -- <br/>Ch. 18. High-dimensional problems: p >> N. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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.<br/><br/>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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
555 |
| Topical term or geographic name entry element |
Statistics |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
3159 |
| Topical term or geographic name entry element |
Mathematical statistics |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
794 |
| Topical term or geographic name entry element |
Data mining |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
3160 |
| Topical term or geographic name entry element |
Bioinformatics |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
3161 |
| Topical term or geographic name entry element |
Computational intelligence |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Relator code |
aut |
| 9 (RLIN) |
3162 |
| Personal name |
Tibshirani, Robert |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Relator code |
aut |
| 9 (RLIN) |
3163 |
| Personal name |
Friedman, J. H. |
| Fuller form of name |
(Jerome H.) |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| 9 (RLIN) |
3164 |
| Uniform title |
Springer texts in statistics |
| Volume/sequential designation |
692 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |
