Learning from Data: Artificial Intelligence and Statistics V
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
1996
|
| Schriftenreihe: | Lecture Notes in Statistics
112 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. In the early days of the Workshop series it seemed clear that researchers in AI and statistics had common interests, though with different emphases, goals, and vocabularies. In learning and model selection, for example, a historical goal of AI to build autonomous agents probably contributed to a focus on parameter-free learning systems, which relied little on an external analyst's assumptions about the data. This seemed at odds with statistical strategy, which stemmed from a view that model selection methods were tools to augment, not replace, the abilities of a human analyst. Thus, statisticians have traditionally spent considerably more time exploiting prior information of the environment to model data and exploratory data analysis methods tailored to their assumptions. In statistics, special emphasis is placed on model checking, making extensive use of residual analysis, because all models are 'wrong', but some are better than others. It is increasingly recognized that AI researchers and/or AI programs can exploit the same kind of statistical strategies to good effect. Often AI researchers and statisticians emphasized different aspects of what in retrospect we might now regard as the same overriding tasks |
| Beschreibung: | 1 Online-Ressource (450p) |
| ISBN: | 9781461224044 9780387947365 |
| ISSN: | 0930-0325 |
| DOI: | 10.1007/978-1-4612-2404-4 |
Internformat
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| 500 | |a Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. In the early days of the Workshop series it seemed clear that researchers in AI and statistics had common interests, though with different emphases, goals, and vocabularies. In learning and model selection, for example, a historical goal of AI to build autonomous agents probably contributed to a focus on parameter-free learning systems, which relied little on an external analyst's assumptions about the data. This seemed at odds with statistical strategy, which stemmed from a view that model selection methods were tools to augment, not replace, the abilities of a human analyst. Thus, statisticians have traditionally spent considerably more time exploiting prior information of the environment to model data and exploratory data analysis methods tailored to their assumptions. In statistics, special emphasis is placed on model checking, making extensive use of residual analysis, because all models are 'wrong', but some are better than others. It is increasingly recognized that AI researchers and/or AI programs can exploit the same kind of statistical strategies to good effect. Often AI researchers and statisticians emphasized different aspects of what in retrospect we might now regard as the same overriding tasks | ||
| 650 | 4 | |a Statistics | |
| 650 | 4 | |a Statistics, general | |
| 650 | 4 | |a Statistik | |
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Datensatz im Suchindex
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| any_adam_object | |
| author | Fisher, Doug |
| author_facet | Fisher, Doug |
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| author_sort | Fisher, Doug |
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| building | Verbundindex |
| bvnumber | BV042420020 |
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| dewey-full | 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5 |
| dewey-search | 519.5 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4612-2404-4 |
| format | Electronic eBook |
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| spelling | Fisher, Doug Verfasser aut Learning from Data Artificial Intelligence and Statistics V edited by Doug Fisher, Hans-J. Lenz New York, NY Springer New York 1996 1 Online-Ressource (450p) txt rdacontent c rdamedia cr rdacarrier Lecture Notes in Statistics 112 0930-0325 Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. In the early days of the Workshop series it seemed clear that researchers in AI and statistics had common interests, though with different emphases, goals, and vocabularies. In learning and model selection, for example, a historical goal of AI to build autonomous agents probably contributed to a focus on parameter-free learning systems, which relied little on an external analyst's assumptions about the data. This seemed at odds with statistical strategy, which stemmed from a view that model selection methods were tools to augment, not replace, the abilities of a human analyst. Thus, statisticians have traditionally spent considerably more time exploiting prior information of the environment to model data and exploratory data analysis methods tailored to their assumptions. In statistics, special emphasis is placed on model checking, making extensive use of residual analysis, because all models are 'wrong', but some are better than others. It is increasingly recognized that AI researchers and/or AI programs can exploit the same kind of statistical strategies to good effect. Often AI researchers and statisticians emphasized different aspects of what in retrospect we might now regard as the same overriding tasks Statistics Statistics, general Statistik Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf 1\p (DE-588)1071861417 Konferenzschrift 1995 Fort Lauderdale Fla. gnd-content Künstliche Intelligenz (DE-588)4033447-8 s Statistik (DE-588)4056995-0 s 2\p DE-604 Lenz, Hans-J. Sonstige oth https://doi.org/10.1007/978-1-4612-2404-4 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Fisher, Doug Learning from Data Artificial Intelligence and Statistics V Statistics Statistics, general Statistik Künstliche Intelligenz (DE-588)4033447-8 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4056995-0 (DE-588)1071861417 |
| title | Learning from Data Artificial Intelligence and Statistics V |
| title_auth | Learning from Data Artificial Intelligence and Statistics V |
| title_exact_search | Learning from Data Artificial Intelligence and Statistics V |
| title_full | Learning from Data Artificial Intelligence and Statistics V edited by Doug Fisher, Hans-J. Lenz |
| title_fullStr | Learning from Data Artificial Intelligence and Statistics V edited by Doug Fisher, Hans-J. Lenz |
| title_full_unstemmed | Learning from Data Artificial Intelligence and Statistics V edited by Doug Fisher, Hans-J. Lenz |
| title_short | Learning from Data |
| title_sort | learning from data artificial intelligence and statistics v |
| title_sub | Artificial Intelligence and Statistics V |
| topic | Statistics Statistics, general Statistik Künstliche Intelligenz (DE-588)4033447-8 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Statistics Statistics, general Statistik Künstliche Intelligenz Konferenzschrift 1995 Fort Lauderdale Fla. |
| url | https://doi.org/10.1007/978-1-4612-2404-4 |
| work_keys_str_mv | AT fisherdoug learningfromdataartificialintelligenceandstatisticsv AT lenzhansj learningfromdataartificialintelligenceandstatisticsv |