Foundations of Knowledge Acquisition: Machine Learning
One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current computers do not. Yet if we could devise lear...
Gespeichert in:
| Weitere Verfasser: | , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1993
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| Schriftenreihe: | The Springer International Series in Engineering and Computer Science
195 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current computers do not. Yet if we could devise learning techniques that enable computers to routinely improve their performance through experience, the impact would be enormous. The result would be an explosion of new computer applications that would suddenly become economically feasible (e. g. , personalized computer assistants that automatically tune themselves to the needs of individual users), and a dramatic improvement in the quality of current computer applications (e. g. , imagine an airline scheduling program that improves its scheduling method based on analyzing past delays). And while the potential economic impact of successful learning methods is sufficient reason to invest in research into machine learning, there is a second significant reason: studying machine learning helps us understand our own human learning abilities and disabilities, leading to the possibility of improved methods in education. While many open questions remain about the methods by which machines and humans might learn, significant progress has been made |
| Beschreibung: | 1 Online-Ressource (XII, 334 p) |
| ISBN: | 9780585273662 |
| DOI: | 10.1007/b102257 |
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| 520 | |a One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current computers do not. Yet if we could devise learning techniques that enable computers to routinely improve their performance through experience, the impact would be enormous. The result would be an explosion of new computer applications that would suddenly become economically feasible (e. g. , personalized computer assistants that automatically tune themselves to the needs of individual users), and a dramatic improvement in the quality of current computer applications (e. g. , imagine an airline scheduling program that improves its scheduling method based on analyzing past delays). And while the potential economic impact of successful learning methods is sufficient reason to invest in research into machine learning, there is a second significant reason: studying machine learning helps us understand our own human learning abilities and disabilities, leading to the possibility of improved methods in education. While many open questions remain about the methods by which machines and humans might learn, significant progress has been made | ||
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Datensatz im Suchindex
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| author2_role | edt edt |
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| discipline | Informatik |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:59Z |
| institution | BVB |
| isbn | 9780585273662 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030576775 |
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| physical | 1 Online-Ressource (XII, 334 p) |
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| publishDate | 1993 |
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| publisher | Springer US |
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| series2 | The Springer International Series in Engineering and Computer Science |
| spelling | Foundations of Knowledge Acquisition Machine Learning edited by Alan L. Meyrowitz, Susan Chipman Boston, MA Springer US 1993 1 Online-Ressource (XII, 334 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science 195 One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current computers do not. Yet if we could devise learning techniques that enable computers to routinely improve their performance through experience, the impact would be enormous. The result would be an explosion of new computer applications that would suddenly become economically feasible (e. g. , personalized computer assistants that automatically tune themselves to the needs of individual users), and a dramatic improvement in the quality of current computer applications (e. g. , imagine an airline scheduling program that improves its scheduling method based on analyzing past delays). And while the potential economic impact of successful learning methods is sufficient reason to invest in research into machine learning, there is a second significant reason: studying machine learning helps us understand our own human learning abilities and disabilities, leading to the possibility of improved methods in education. While many open questions remain about the methods by which machines and humans might learn, significant progress has been made Computer Science Artificial Intelligence (incl. Robotics) Computer Science, general Computer science Artificial intelligence Meyrowitz, Alan L. edt Chipman, Susan edt Erscheint auch als Druck-Ausgabe 9780792392781 https://doi.org/10.1007/b102257 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Foundations of Knowledge Acquisition Machine Learning Computer Science Artificial Intelligence (incl. Robotics) Computer Science, general Computer science Artificial intelligence |
| title | Foundations of Knowledge Acquisition Machine Learning |
| title_auth | Foundations of Knowledge Acquisition Machine Learning |
| title_exact_search | Foundations of Knowledge Acquisition Machine Learning |
| title_full | Foundations of Knowledge Acquisition Machine Learning edited by Alan L. Meyrowitz, Susan Chipman |
| title_fullStr | Foundations of Knowledge Acquisition Machine Learning edited by Alan L. Meyrowitz, Susan Chipman |
| title_full_unstemmed | Foundations of Knowledge Acquisition Machine Learning edited by Alan L. Meyrowitz, Susan Chipman |
| title_short | Foundations of Knowledge Acquisition |
| title_sort | foundations of knowledge acquisition machine learning |
| title_sub | Machine Learning |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Computer Science, general Computer science Artificial intelligence |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Computer Science, general Computer science Artificial intelligence |
| url | https://doi.org/10.1007/b102257 |
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