The Elements of Statistical Learning (Record no. 17994)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04614nam a22005055i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-0-387-84858-7 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200712171050.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 100301s2009 xxu| s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780387848587 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1007/978-0-387-84858-7 |
| Source of number or code | doi |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | BT-SaRUSC |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3 |
| Edition number | 23 |
| Item number | HAS |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Hastie, Trevor. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 245 14 - TITLE STATEMENT | |
| Title | The Elements of Statistical Learning |
| Medium | [electronic resource] : |
| Remainder of title | Data Mining, Inference, and Prediction, Second Edition / |
| Statement of responsibility, etc | by Trevor Hastie, Robert Tibshirani, Jerome Friedman. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd ed. 2009. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | XXII, 745 p. 658 illus. |
| Other physical details | online resource. |
| Size of unit | 610 MB |
| 490 1# - SERIES STATEMENT | |
| Series statement | Springer Series in Statistics, |
| International Standard Serial Number | 0172-7397 |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Overview of Supervised Learning -- Linear Methods for Regression -- Linear Methods for Classification -- Basis Expansions and Regularization -- Kernel Smoothing Methods -- Model Assessment and Selection -- Model Inference and Averaging -- Additive Models, Trees, and Related Methods -- Boosting and Additive Trees -- Neural Networks -- Support Vector Machines and Flexible Discriminants -- Prototype Methods and Nearest-Neighbors -- Unsupervised Learning -- Random Forests -- Ensemble Learning -- Undirected Graphical Models -- 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. 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. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Probabilities. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Statistics . |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Bioinformatics. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Bioinformatics . |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computational biology . |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data Mining and Knowledge Discovery. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Probability Theory and Stochastic Processes. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Statistical Theory and Methods. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computational Biology/Bioinformatics. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computer Appl. in Life Sciences. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Tibshirani, Robert. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Friedman, Jerome. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY | |
| Title | Springer eBooks |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9780387848846 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9780387848570 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | Springer Series in Statistics, |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://doi.org/10.1007/978-0-387-84858-7">https://doi.org/10.1007/978-0-387-84858-7</a> |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://library.sce.edu.bt/cgi-bin/koha/opac-retrieve-file.pl?id=18922b2800ef6c777a9a6d3dbad357b0">http://library.sce.edu.bt/cgi-bin/koha/opac-retrieve-file.pl?id=18922b2800ef6c777a9a6d3dbad357b0</a> |
| Link text | Ebook |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Computer Files |
No items available.