Neural network learning: theoretical foundations
This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Research on pattern classification with binary-output networks is surveyed, including...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
1999
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| Subjects: | |
| Online Access: | BSB01 FHN01 UER01 UPA01 Volltext |
| Summary: | This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Research on pattern classification with binary-output networks is surveyed, including a discussion of the relevance of the Vapnik–Chervonenkis dimension, and calculating estimates of the dimension for several neural network models. A model of classification by real-output networks is developed, and the usefulness of classification with a 'large margin' is demonstrated. The authors explain the role of scale-sensitive versions of the Vapnik–Chervonenkis dimension in large margin classification, and in real prediction. They also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient constructive learning algorithms. The book is self-contained and is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics |
| Item Description: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Physical Description: | 1 online resource (xiv, 389 pages) |
| ISBN: | 9780511624216 |
| DOI: | 10.1017/CBO9780511624216 |
Staff View
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| 520 | |a This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Research on pattern classification with binary-output networks is surveyed, including a discussion of the relevance of the Vapnik–Chervonenkis dimension, and calculating estimates of the dimension for several neural network models. A model of classification by real-output networks is developed, and the usefulness of classification with a 'large margin' is demonstrated. The authors explain the role of scale-sensitive versions of the Vapnik–Chervonenkis dimension in large margin classification, and in real prediction. They also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient constructive learning algorithms. The book is self-contained and is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics | ||
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| id | DE-604.BV043943141 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:39:19Z |
| institution | BVB |
| isbn | 9780511624216 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029352112 |
| oclc_num | 967602699 |
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| physical | 1 online resource (xiv, 389 pages) |
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| publishDate | 1999 |
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| spelling | Anthony, Martin 1967- Verfasser (DE-588)114372675 aut Neural network learning theoretical foundations Martin Anthony and Peter L. Bartlett Cambridge Cambridge University Press 1999 1 online resource (xiv, 389 pages) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Research on pattern classification with binary-output networks is surveyed, including a discussion of the relevance of the Vapnik–Chervonenkis dimension, and calculating estimates of the dimension for several neural network models. A model of classification by real-output networks is developed, and the usefulness of classification with a 'large margin' is demonstrated. The authors explain the role of scale-sensitive versions of the Vapnik–Chervonenkis dimension in large margin classification, and in real prediction. They also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient constructive learning algorithms. The book is self-contained and is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics Neural networks (Computer science) Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Bartlett, Peter L. 1966- Sonstige (DE-588)140240780 oth Erscheint auch als Druck-Ausgabe, Hardcover 978-0-521-57353-5 Erscheint auch als Druck-Ausgabe, Paperback 978-0-521-11862-0 https://doi.org/10.1017/CBO9780511624216 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Anthony, Martin 1967- Neural network learning theoretical foundations Neural networks (Computer science) Neuronales Netz (DE-588)4226127-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4226127-2 (DE-588)4193754-5 |
| title | Neural network learning theoretical foundations |
| title_auth | Neural network learning theoretical foundations |
| title_exact_search | Neural network learning theoretical foundations |
| title_full | Neural network learning theoretical foundations Martin Anthony and Peter L. Bartlett |
| title_fullStr | Neural network learning theoretical foundations Martin Anthony and Peter L. Bartlett |
| title_full_unstemmed | Neural network learning theoretical foundations Martin Anthony and Peter L. Bartlett |
| title_short | Neural network learning |
| title_sort | neural network learning theoretical foundations |
| title_sub | theoretical foundations |
| topic | Neural networks (Computer science) Neuronales Netz (DE-588)4226127-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Neural networks (Computer science) Neuronales Netz Maschinelles Lernen |
| url | https://doi.org/10.1017/CBO9780511624216 |
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