Statistical learning for biomedical data:
This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if t...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2011
|
| Schriftenreihe: | Practical guides to biostatistics and epidemiology
|
| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 UER01 Volltext |
| Zusammenfassung: | This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests™, neural nets, support vector machines, nearest neighbors and boosting |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (xii, 285 pages) |
| ISBN: | 9780511975820 |
| DOI: | 10.1017/CBO9780511975820 |
Internformat
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Datensatz im Suchindex
| _version_ | 1804176881427152896 |
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| any_adam_object | |
| author | Malley, James D. Malley, Karen G. Pajevic, Sinisa |
| author_facet | Malley, James D. Malley, Karen G. Pajevic, Sinisa |
| author_role | aut aut aut |
| author_sort | Malley, James D. |
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| building | Verbundindex |
| bvnumber | BV043940640 |
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| dewey-full | 614.285 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 614 - Forensic medicine; incidence of disease |
| dewey-raw | 614.285 |
| dewey-search | 614.285 |
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| dewey-tens | 610 - Medicine and health |
| discipline | Medizin |
| doi_str_mv | 10.1017/CBO9780511975820 |
| format | Electronic eBook |
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| id | DE-604.BV043940640 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:39:14Z |
| institution | BVB |
| isbn | 9780511975820 |
| language | English |
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| publisher | Cambridge University Press |
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| series2 | Practical guides to biostatistics and epidemiology |
| spelling | Malley, James D. Verfasser aut Statistical learning for biomedical data James D. Malley, Karen G. Malley, Sinisa Pajevic Cambridge Cambridge University Press 2011 1 online resource (xii, 285 pages) txt rdacontent c rdamedia cr rdacarrier Practical guides to biostatistics and epidemiology Title from publisher's bibliographic system (viewed on 05 Oct 2015) This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests™, neural nets, support vector machines, nearest neighbors and boosting Datenverarbeitung Medical statistics / Data processing Biometry / Data processing Epidemiologie (DE-588)4015016-1 gnd rswk-swf Medizinische Statistik (DE-588)4127563-9 gnd rswk-swf Biostatistik (DE-588)4729990-3 gnd rswk-swf Biostatistik (DE-588)4729990-3 s Medizinische Statistik (DE-588)4127563-9 s Epidemiologie (DE-588)4015016-1 s 1\p DE-604 Malley, Karen G. Verfasser aut Pajevic, Sinisa Verfasser aut Erscheint auch als Druck-Ausgabe, Hardcover 978-0-521-87580-6 Erscheint auch als Druck-Ausgabe, Paperback 978-0-521-69909-9 https://doi.org/10.1017/CBO9780511975820 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Malley, James D. Malley, Karen G. Pajevic, Sinisa Statistical learning for biomedical data Datenverarbeitung Medical statistics / Data processing Biometry / Data processing Epidemiologie (DE-588)4015016-1 gnd Medizinische Statistik (DE-588)4127563-9 gnd Biostatistik (DE-588)4729990-3 gnd |
| subject_GND | (DE-588)4015016-1 (DE-588)4127563-9 (DE-588)4729990-3 |
| title | Statistical learning for biomedical data |
| title_auth | Statistical learning for biomedical data |
| title_exact_search | Statistical learning for biomedical data |
| title_full | Statistical learning for biomedical data James D. Malley, Karen G. Malley, Sinisa Pajevic |
| title_fullStr | Statistical learning for biomedical data James D. Malley, Karen G. Malley, Sinisa Pajevic |
| title_full_unstemmed | Statistical learning for biomedical data James D. Malley, Karen G. Malley, Sinisa Pajevic |
| title_short | Statistical learning for biomedical data |
| title_sort | statistical learning for biomedical data |
| topic | Datenverarbeitung Medical statistics / Data processing Biometry / Data processing Epidemiologie (DE-588)4015016-1 gnd Medizinische Statistik (DE-588)4127563-9 gnd Biostatistik (DE-588)4729990-3 gnd |
| topic_facet | Datenverarbeitung Medical statistics / Data processing Biometry / Data processing Epidemiologie Medizinische Statistik Biostatistik |
| url | https://doi.org/10.1017/CBO9780511975820 |
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