Learning and Generalisation: With Applications to Neural Networks
Learning and Generalization provides a formal mathematical theory for addressing intuitive questions such as: • How does a machine learn a new concept on the basis of examples? • How can a neural network, after sufficient training, correctly predict the outcome of a previously unseen input? • How mu...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
London
Springer London
2003
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| Schriftenreihe: | Communications and Control Engineering
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| Schlagworte: | |
| Online-Zugang: | FHI01 BTU01 Volltext |
| Zusammenfassung: | Learning and Generalization provides a formal mathematical theory for addressing intuitive questions such as: • How does a machine learn a new concept on the basis of examples? • How can a neural network, after sufficient training, correctly predict the outcome of a previously unseen input? • How much training is required to achieve a specified level of accuracy in the prediction? • How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite interval of time? In its successful first edition, A Theory of Learning and Generalization was the first book to treat the problem of machine learning in conjunction with the theory of empirical processes, the latter being a well-established branch of probability theory. The treatment of both topics side-by-side leads to new insights, as well as to new results in both topics. This second edition extends and improves upon this material, covering new areas including: • Support vector machines. • Fat-shattering dimensions and applications to neural network learning. • Learning with dependent samples generated by a beta-mixing process. • Connections between system identification and learning theory. • Probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithm. Reflecting advancements in the field, solutions to some of the open problems posed in the first edition are presented, while new open problems have been added. Learning and Generalization (second edition) is essential reading for control and system theorists, neural network researchers, theoretical computer scientists and probabilist |
| Beschreibung: | 1 Online-Ressource (XXI, 488 p) |
| ISBN: | 9781447137481 |
| DOI: | 10.1007/978-1-4471-3748-1 |
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Datensatz im Suchindex
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| isbn | 9781447137481 |
| language | English |
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| spelling | Vidyasagar, M. Verfasser aut Learning and Generalisation With Applications to Neural Networks by M. Vidyasagar London Springer London 2003 1 Online-Ressource (XXI, 488 p) txt rdacontent c rdamedia cr rdacarrier Communications and Control Engineering Learning and Generalization provides a formal mathematical theory for addressing intuitive questions such as: • How does a machine learn a new concept on the basis of examples? • How can a neural network, after sufficient training, correctly predict the outcome of a previously unseen input? • How much training is required to achieve a specified level of accuracy in the prediction? • How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite interval of time? In its successful first edition, A Theory of Learning and Generalization was the first book to treat the problem of machine learning in conjunction with the theory of empirical processes, the latter being a well-established branch of probability theory. The treatment of both topics side-by-side leads to new insights, as well as to new results in both topics. This second edition extends and improves upon this material, covering new areas including: • Support vector machines. • Fat-shattering dimensions and applications to neural network learning. • Learning with dependent samples generated by a beta-mixing process. • Connections between system identification and learning theory. • Probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithm. Reflecting advancements in the field, solutions to some of the open problems posed in the first edition are presented, while new open problems have been added. Learning and Generalization (second edition) is essential reading for control and system theorists, neural network researchers, theoretical computer scientists and probabilist Engineering Electrical Engineering Control Systems Theory, Control Probability Theory and Stochastic Processes Group Theory and Generalizations Computer Communication Networks Computer communication systems Group theory System theory Probabilities Control engineering Electrical engineering Mathematische Lerntheorie (DE-588)4169103-9 gnd rswk-swf Mathematische Lerntheorie (DE-588)4169103-9 s 1\p DE-604 Erscheint auch als Druck-Ausgabe 9781849968676 https://doi.org/10.1007/978-1-4471-3748-1 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Vidyasagar, M. Learning and Generalisation With Applications to Neural Networks Engineering Electrical Engineering Control Systems Theory, Control Probability Theory and Stochastic Processes Group Theory and Generalizations Computer Communication Networks Computer communication systems Group theory System theory Probabilities Control engineering Electrical engineering Mathematische Lerntheorie (DE-588)4169103-9 gnd |
| subject_GND | (DE-588)4169103-9 |
| title | Learning and Generalisation With Applications to Neural Networks |
| title_auth | Learning and Generalisation With Applications to Neural Networks |
| title_exact_search | Learning and Generalisation With Applications to Neural Networks |
| title_full | Learning and Generalisation With Applications to Neural Networks by M. Vidyasagar |
| title_fullStr | Learning and Generalisation With Applications to Neural Networks by M. Vidyasagar |
| title_full_unstemmed | Learning and Generalisation With Applications to Neural Networks by M. Vidyasagar |
| title_short | Learning and Generalisation |
| title_sort | learning and generalisation with applications to neural networks |
| title_sub | With Applications to Neural Networks |
| topic | Engineering Electrical Engineering Control Systems Theory, Control Probability Theory and Stochastic Processes Group Theory and Generalizations Computer Communication Networks Computer communication systems Group theory System theory Probabilities Control engineering Electrical engineering Mathematische Lerntheorie (DE-588)4169103-9 gnd |
| topic_facet | Engineering Electrical Engineering Control Systems Theory, Control Probability Theory and Stochastic Processes Group Theory and Generalizations Computer Communication Networks Computer communication systems Group theory System theory Probabilities Control engineering Electrical engineering Mathematische Lerntheorie |
| url | https://doi.org/10.1007/978-1-4471-3748-1 |
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