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...
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| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
1999
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| Links: | https://doi.org/10.1017/CBO9780511624216 |
| Zusammenfassung: | 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. |
| Umfang: | 1 Online-Ressource (xiv, 389 Seiten) |
| ISBN: | 9780511624216 |
Internformat
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| spelling | Anthony, Martin Neural network learning theoretical foundations Martin Anthony and Peter L. Bartlett Cambridge Cambridge University Press 1999 1 Online-Ressource (xiv, 389 Seiten) txt c cr 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. Bartlett, Peter L. 1966- Erscheint auch als Druck-Ausgabe 9780521118620 Erscheint auch als Druck-Ausgabe 9780521573535 |
| spellingShingle | Anthony, Martin Neural network learning theoretical foundations |
| 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 |
| work_keys_str_mv | AT anthonymartin neuralnetworklearningtheoreticalfoundations AT bartlettpeterl neuralnetworklearningtheoreticalfoundations |