Machine Learning of Inductive Bias:
This book is based on the author's Ph.D. dissertation[56]. The the sis research was conducted while the author was a graduate student in the Department of Computer Science at Rutgers University. The book was pre pared at the University of Massachusetts at Amherst where the author is currently...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1986
|
| Schriftenreihe: | The Kluwer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems
15 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | This book is based on the author's Ph.D. dissertation[56]. The the sis research was conducted while the author was a graduate student in the Department of Computer Science at Rutgers University. The book was pre pared at the University of Massachusetts at Amherst where the author is currently an Assistant Professor in the Department of Computer and Infor mation Science. Programs that learn concepts from examples are guided not only by the examples (and counterexamples) that they observe, but also by bias that determines which concept is to be considered as following best from the ob servations. Selection of a concept represents an inductive leap because the concept then indicates the classification of instances that have not yet been observed by the learning program. Learning programs that make undesir able inductive leaps do so due to undesirable bias. The research problem addressed here is to show how a learning program can learn a desirable inductive bias |
| Beschreibung: | 1 Online-Ressource (XVIII, 166 p) |
| ISBN: | 9781461322832 |
| DOI: | 10.1007/978-1-4613-2283-2 |
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| spelling | Utgoff, Paul E. Verfasser aut Machine Learning of Inductive Bias by Paul E. Utgoff Boston, MA Springer US 1986 1 Online-Ressource (XVIII, 166 p) txt rdacontent c rdamedia cr rdacarrier The Kluwer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems 15 This book is based on the author's Ph.D. dissertation[56]. The the sis research was conducted while the author was a graduate student in the Department of Computer Science at Rutgers University. The book was pre pared at the University of Massachusetts at Amherst where the author is currently an Assistant Professor in the Department of Computer and Infor mation Science. Programs that learn concepts from examples are guided not only by the examples (and counterexamples) that they observe, but also by bias that determines which concept is to be considered as following best from the ob servations. Selection of a concept represents an inductive leap because the concept then indicates the classification of instances that have not yet been observed by the learning program. Learning programs that make undesir able inductive leaps do so due to undesirable bias. The research problem addressed here is to show how a learning program can learn a desirable inductive bias Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Integralrechnung (DE-588)4027232-1 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Lernender Automat (DE-588)4167398-0 gnd rswk-swf 1\p (DE-588)4113937-9 Hochschulschrift gnd-content Künstliche Intelligenz (DE-588)4033447-8 s Integralrechnung (DE-588)4027232-1 s 2\p DE-604 Lernender Automat (DE-588)4167398-0 s 3\p DE-604 Erscheint auch als Druck-Ausgabe 9781461294085 https://doi.org/10.1007/978-1-4613-2283-2 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 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Utgoff, Paul E. Machine Learning of Inductive Bias Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Integralrechnung (DE-588)4027232-1 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Lernender Automat (DE-588)4167398-0 gnd |
| subject_GND | (DE-588)4027232-1 (DE-588)4033447-8 (DE-588)4167398-0 (DE-588)4113937-9 |
| title | Machine Learning of Inductive Bias |
| title_auth | Machine Learning of Inductive Bias |
| title_exact_search | Machine Learning of Inductive Bias |
| title_full | Machine Learning of Inductive Bias by Paul E. Utgoff |
| title_fullStr | Machine Learning of Inductive Bias by Paul E. Utgoff |
| title_full_unstemmed | Machine Learning of Inductive Bias by Paul E. Utgoff |
| title_short | Machine Learning of Inductive Bias |
| title_sort | machine learning of inductive bias |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Integralrechnung (DE-588)4027232-1 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Lernender Automat (DE-588)4167398-0 gnd |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Integralrechnung Künstliche Intelligenz Lernender Automat Hochschulschrift |
| url | https://doi.org/10.1007/978-1-4613-2283-2 |
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