The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
New York, NY
Springer New York
2001
|
| Schriftenreihe: | Springer Series in Statistics
|
| Schlagwörter: | |
| Links: | https://doi.org/10.1007/978-0-387-21606-5 |
| Beschreibung: | 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 |
| Umfang: | 1 Online-Ressource (XVI, 536 p) |
| ISBN: | 9780387216065 9781489905192 |
| ISSN: | 0172-7397 |
| DOI: | 10.1007/978-0-387-21606-5 |
Internformat
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| 500 | |a 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. | ||
| 500 | |a 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 | ||
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Datensatz im Suchindex
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| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-0-387-21606-5 |
| format | Electronic eBook |
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| id | DE-604.BV042418931 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T17:10:39Z |
| institution | BVB |
| isbn | 9780387216065 9781489905192 |
| issn | 0172-7397 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027854348 |
| oclc_num | 858997631 |
| open_access_boolean | |
| owner | DE-384 DE-703 DE-91 DE-BY-TUM DE-634 |
| owner_facet | DE-384 DE-703 DE-91 DE-BY-TUM DE-634 |
| physical | 1 Online-Ressource (XVI, 536 p) |
| psigel | ZDB-2-SMA ZDB-2-BAE ZDB-2-SMA_Archive |
| publishDate | 2001 |
| publishDateSearch | 2001 |
| publishDateSort | 2001 |
| publisher | Springer New York |
| record_format | marc |
| series2 | Springer Series in Statistics |
| spellingShingle | Hastie, Trevor 1953- The Elements of Statistical Learning Data Mining, Inference, and Prediction Statistics Computer science Database management Artificial intelligence Biology / Data processing Mathematical statistics Statistical Theory and Methods Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Probability and Statistics in Computer Science Computer Appl. in Life Sciences Database Management Artificial Intelligence (incl. Robotics) Datenverarbeitung Informatik Künstliche Intelligenz Statistik Datenanalyse (DE-588)4123037-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4193754-5 (DE-588)4056995-0 |
| title | The Elements of Statistical Learning Data Mining, Inference, and Prediction |
| title_auth | The Elements of Statistical Learning Data Mining, Inference, and Prediction |
| title_exact_search | The Elements of Statistical Learning Data Mining, Inference, and Prediction |
| title_full | The Elements of Statistical Learning Data Mining, Inference, and Prediction by Trevor Hastie, Jerome Friedman, Robert Tibshirani |
| title_fullStr | The Elements of Statistical Learning Data Mining, Inference, and Prediction by Trevor Hastie, Jerome Friedman, Robert Tibshirani |
| title_full_unstemmed | The Elements of Statistical Learning Data Mining, Inference, and Prediction by Trevor Hastie, Jerome Friedman, Robert Tibshirani |
| title_short | The Elements of Statistical Learning |
| title_sort | the elements of statistical learning data mining inference and prediction |
| title_sub | Data Mining, Inference, and Prediction |
| topic | Statistics Computer science Database management Artificial intelligence Biology / Data processing Mathematical statistics Statistical Theory and Methods Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Probability and Statistics in Computer Science Computer Appl. in Life Sciences Database Management Artificial Intelligence (incl. Robotics) Datenverarbeitung Informatik Künstliche Intelligenz Statistik Datenanalyse (DE-588)4123037-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Statistics Computer science Database management Artificial intelligence Biology / Data processing Mathematical statistics Statistical Theory and Methods Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Probability and Statistics in Computer Science Computer Appl. in Life Sciences Database Management Artificial Intelligence (incl. Robotics) Datenverarbeitung Informatik Künstliche Intelligenz Statistik Datenanalyse Maschinelles Lernen |
| url | https://doi.org/10.1007/978-0-387-21606-5 |
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