Learning with Nested Generalized Exemplars:
Machine Learning is one of the oldest and most intriguing areas of Ar tificial Intelligence. From the moment that computer visionaries first began to conceive the potential for general-purpose symbolic computa tion, the concept of a machine that could learn by itself has been an ever present goal....
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1990
|
| Schriftenreihe: | The Kluwer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems
100 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | Machine Learning is one of the oldest and most intriguing areas of Ar tificial Intelligence. From the moment that computer visionaries first began to conceive the potential for general-purpose symbolic computa tion, the concept of a machine that could learn by itself has been an ever present goal. Today, although there have been many implemented com puter programs that can be said to learn, we are still far from achieving the lofty visions of self-organizing automata that spring to mind when we think of machine learning. We have established some base camps and scaled some of the foothills of this epic intellectual adventure, but we are still far from the lofty peaks that the imagination conjures up. Nevertheless, a solid foundation of theory and technique has begun to develop around a variety of specialized learning tasks. Such tasks in clude discovery of optimal or effective parameter settings for controlling processes, automatic acquisition or refinement of rules for controlling behavior in rule-driven systems, and automatic classification and di agnosis of items on the basis of their features. Contributions include algorithms for optimal parameter estimation, feedback and adaptation algorithms, strategies for credit/blame assignment, techniques for rule and category acquisition, theoretical results dealing with learnability of various classes by formal automata, and empirical investigations of the abilities of many different learning algorithms in a diversity of applica tion areas |
| Beschreibung: | 1 Online-Ressource (XX, 160 p) |
| ISBN: | 9781461315490 |
| DOI: | 10.1007/978-1-4613-1549-0 |
Internformat
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| 520 | |a Machine Learning is one of the oldest and most intriguing areas of Ar tificial Intelligence. From the moment that computer visionaries first began to conceive the potential for general-purpose symbolic computa tion, the concept of a machine that could learn by itself has been an ever present goal. Today, although there have been many implemented com puter programs that can be said to learn, we are still far from achieving the lofty visions of self-organizing automata that spring to mind when we think of machine learning. We have established some base camps and scaled some of the foothills of this epic intellectual adventure, but we are still far from the lofty peaks that the imagination conjures up. Nevertheless, a solid foundation of theory and technique has begun to develop around a variety of specialized learning tasks. Such tasks in clude discovery of optimal or effective parameter settings for controlling processes, automatic acquisition or refinement of rules for controlling behavior in rule-driven systems, and automatic classification and di agnosis of items on the basis of their features. Contributions include algorithms for optimal parameter estimation, feedback and adaptation algorithms, strategies for credit/blame assignment, techniques for rule and category acquisition, theoretical results dealing with learnability of various classes by formal automata, and empirical investigations of the abilities of many different learning algorithms in a diversity of applica tion areas | ||
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Datensatz im Suchindex
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| author | Salzberg, Steven L. 1960- |
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| discipline | Informatik |
| doi_str_mv | 10.1007/978-1-4613-1549-0 |
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| series2 | The Kluwer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems |
| spelling | Salzberg, Steven L. 1960- Verfasser (DE-588)135643783 aut Learning with Nested Generalized Exemplars by Steven L. Salzberg Boston, MA Springer US 1990 1 Online-Ressource (XX, 160 p) txt rdacontent c rdamedia cr rdacarrier The Kluwer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems 100 Machine Learning is one of the oldest and most intriguing areas of Ar tificial Intelligence. From the moment that computer visionaries first began to conceive the potential for general-purpose symbolic computa tion, the concept of a machine that could learn by itself has been an ever present goal. Today, although there have been many implemented com puter programs that can be said to learn, we are still far from achieving the lofty visions of self-organizing automata that spring to mind when we think of machine learning. We have established some base camps and scaled some of the foothills of this epic intellectual adventure, but we are still far from the lofty peaks that the imagination conjures up. Nevertheless, a solid foundation of theory and technique has begun to develop around a variety of specialized learning tasks. Such tasks in clude discovery of optimal or effective parameter settings for controlling processes, automatic acquisition or refinement of rules for controlling behavior in rule-driven systems, and automatic classification and di agnosis of items on the basis of their features. Contributions include algorithms for optimal parameter estimation, feedback and adaptation algorithms, strategies for credit/blame assignment, techniques for rule and category acquisition, theoretical results dealing with learnability of various classes by formal automata, and empirical investigations of the abilities of many different learning algorithms in a diversity of applica tion areas Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Geschachtelte Relation (DE-588)4212689-7 gnd rswk-swf 1\p (DE-588)4113937-9 Hochschulschrift gnd-content Maschinelles Lernen (DE-588)4193754-5 s Geschachtelte Relation (DE-588)4212689-7 s 2\p DE-604 Erscheint auch als Druck-Ausgabe 9781461288305 https://doi.org/10.1007/978-1-4613-1549-0 Verlag URL des Erstveröffentlichers 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 | Salzberg, Steven L. 1960- Learning with Nested Generalized Exemplars Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Maschinelles Lernen (DE-588)4193754-5 gnd Geschachtelte Relation (DE-588)4212689-7 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4212689-7 (DE-588)4113937-9 |
| title | Learning with Nested Generalized Exemplars |
| title_auth | Learning with Nested Generalized Exemplars |
| title_exact_search | Learning with Nested Generalized Exemplars |
| title_full | Learning with Nested Generalized Exemplars by Steven L. Salzberg |
| title_fullStr | Learning with Nested Generalized Exemplars by Steven L. Salzberg |
| title_full_unstemmed | Learning with Nested Generalized Exemplars by Steven L. Salzberg |
| title_short | Learning with Nested Generalized Exemplars |
| title_sort | learning with nested generalized exemplars |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Maschinelles Lernen (DE-588)4193754-5 gnd Geschachtelte Relation (DE-588)4212689-7 gnd |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Maschinelles Lernen Geschachtelte Relation Hochschulschrift |
| url | https://doi.org/10.1007/978-1-4613-1549-0 |
| work_keys_str_mv | AT salzbergstevenl learningwithnestedgeneralizedexemplars |