Sinossi
How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.
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Dalla quarta di copertina
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.
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Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 512 | Sprache: Englisch | Produktart: Bücher | Learning and Generalization provides a formal mathematical theory addressing intuitive questions of the type: ¿ How does a machine learn a concept on the basis of examples? ¿ How can a neural network, after training, correctly predict the outcome of a previously unseen input? ¿ How much training is required to achieve a given 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 time? The second edition covers 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 algorithms. It also contains solutions to some of the open problems posed in the first edition, while adding new open problems. Codice articolo 964957/2
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Comprehensive this book covers all aspects of learning theory and its applications. Other books have a narrower focus  It contains applications not only to neural networks but also to control systems The author has . Codice articolo 4289505
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -How does a machine learn a new concept on the basis of examples This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks. 512 pp. Englisch. Codice articolo 9781852333737
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Buch. Condizione: Neu. Learning and Generalisation | With Applications to Neural Networks | Mathukumalli Vidyasagar | Buch | xxi | Englisch | 2002 | Springer London | EAN 9781852333737 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Codice articolo 105045372
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Buch. Condizione: Neu. Neuware -Learning and Generalization provides a formal mathematical theory addressing intuitive questions of the type:¿ How does a machine learn a concept on the basis of examples ¿ How can a neural network, after training, correctly predict the outcome of a previously unseen input ¿ How much training is required to achieve a given 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 time The second edition covers 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 algorithms.It also contains solutions to some of the open problems posed in the first edition, while adding new open problems.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 512 pp. Englisch. Codice articolo 9781852333737
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Learning and Generalisation : With Applications to Neural Networks
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Learning and Generalization provides a formal mathematical theory addressing intuitive questions of the type: - How does a machine learn a concept on the basis of examples - How can a neural network, after training, correctly predict the outcome of a previously unseen input - How much training is required to achieve a given 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 time The second edition covers 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 algorithms.It also contains solutions to some of the open problems posed in the first edition, while adding new open problems. Codice articolo 9781852333737
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