Prediction, learning, and games:
This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual seque...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2006
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| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 Volltext |
| Zusammenfassung: | This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (xii, 394 Seiten) |
| ISBN: | 9780511546921 |
| DOI: | 10.1017/CBO9780511546921 |
Internformat
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| 500 | |a Title from publisher's bibliographic system (viewed on 05 Oct 2015) | ||
| 520 | |a This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections | ||
| 650 | 4 | |a Game theory | |
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Datensatz im Suchindex
| _version_ | 1804176888605704192 |
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| author | Cesa-Bianchi, Nicolò 1963- Lugosi, Gábor 1964- |
| author_GND | (DE-588)120314797 (DE-588)17173677X |
| author_facet | Cesa-Bianchi, Nicolò 1963- Lugosi, Gábor 1964- |
| author_role | aut aut |
| author_sort | Cesa-Bianchi, Nicolò 1963- |
| author_variant | n c b ncb g l gl |
| building | Verbundindex |
| bvnumber | BV043944088 |
| classification_rvk | QH 233 SK 860 ST 304 |
| collection | ZDB-20-CBO |
| ctrlnum | (ZDB-20-CBO)CR9780511546921 (OCoLC)967778869 (DE-599)BVBBV043944088 |
| dewey-full | 519.3 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.3 |
| dewey-search | 519.3 |
| dewey-sort | 3519.3 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Mathematik Wirtschaftswissenschaften |
| doi_str_mv | 10.1017/CBO9780511546921 |
| format | Electronic eBook |
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| id | DE-604.BV043944088 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:39:20Z |
| institution | BVB |
| isbn | 9780511546921 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029353059 |
| oclc_num | 967778869 |
| open_access_boolean | |
| owner | DE-12 DE-92 DE-83 |
| owner_facet | DE-12 DE-92 DE-83 |
| physical | 1 online resource (xii, 394 Seiten) |
| psigel | ZDB-20-CBO ZDB-20-CBO BSB_PDA_CBO ZDB-20-CBO FHN_PDA_CBO |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Cesa-Bianchi, Nicolò 1963- (DE-588)120314797 aut Prediction, learning, and games Nicolo Cesa-Bianchi, Universit'a degli Studi di Milano ; Gabor Lugosi, Universitat Pompeu Fabra, Barcelona Prediction, Learning, & Games Cambridge Cambridge University Press 2006 1 online resource (xii, 394 Seiten) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections Game theory Machine learning Computer algorithms Spieltheorie (DE-588)4056243-8 gnd rswk-swf Vorhersagetheorie (DE-588)4188671-9 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Spieltheorie (DE-588)4056243-8 s Vorhersagetheorie (DE-588)4188671-9 s Maschinelles Lernen (DE-588)4193754-5 s 1\p DE-604 Lugosi, Gábor 1964- (DE-588)17173677X aut Erscheint auch als Druck-Ausgabe 978-0-521-84108-5 Erscheint auch als Druckausgabe 978-0-521-84108-5 https://doi.org/10.1017/CBO9780511546921 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Cesa-Bianchi, Nicolò 1963- Lugosi, Gábor 1964- Prediction, learning, and games Game theory Machine learning Computer algorithms Spieltheorie (DE-588)4056243-8 gnd Vorhersagetheorie (DE-588)4188671-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4056243-8 (DE-588)4188671-9 (DE-588)4193754-5 |
| title | Prediction, learning, and games |
| title_alt | Prediction, Learning, & Games |
| title_auth | Prediction, learning, and games |
| title_exact_search | Prediction, learning, and games |
| title_full | Prediction, learning, and games Nicolo Cesa-Bianchi, Universit'a degli Studi di Milano ; Gabor Lugosi, Universitat Pompeu Fabra, Barcelona |
| title_fullStr | Prediction, learning, and games Nicolo Cesa-Bianchi, Universit'a degli Studi di Milano ; Gabor Lugosi, Universitat Pompeu Fabra, Barcelona |
| title_full_unstemmed | Prediction, learning, and games Nicolo Cesa-Bianchi, Universit'a degli Studi di Milano ; Gabor Lugosi, Universitat Pompeu Fabra, Barcelona |
| title_short | Prediction, learning, and games |
| title_sort | prediction learning and games |
| topic | Game theory Machine learning Computer algorithms Spieltheorie (DE-588)4056243-8 gnd Vorhersagetheorie (DE-588)4188671-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Game theory Machine learning Computer algorithms Spieltheorie Vorhersagetheorie Maschinelles Lernen |
| url | https://doi.org/10.1017/CBO9780511546921 |
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