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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
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Da: California Books, Miami, FL, U.S.A.
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Da: Ria Christie Collections, Uxbridge, Regno Unito
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Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Nov 2009, 2009
ISBN 10: 3642025315 ISBN 13: 9783642025310
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 106,99
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware -Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters.This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 96 pp. Englisch.
Editore: Springer Berlin Heidelberg, 2012
ISBN 10: 3642261396 ISBN 13: 9783642261398
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 106,99
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters. This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks.
Editore: Springer Berlin Heidelberg, 2009
ISBN 10: 3642025315 ISBN 13: 9783642025310
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 106,99
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Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters. This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks.
Editore: Springer-Verlag New York Inc, 2009
ISBN 10: 3642025315 ISBN 13: 9783642025310
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
EUR 148,02
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Aggiungi al carrelloHardcover. Condizione: Brand New. 1st edition. 130 pages. 9.25x6.25x0.25 inches. In Stock.
Editore: Springer Berlin Heidelberg, 2012
ISBN 10: 3642261396 ISBN 13: 9783642261398
Lingua: Inglese
Da: Buchpark, Trebbin, Germania
Condizione: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher.
Editore: Springer Berlin Heidelberg, 2012
ISBN 10: 3642261396 ISBN 13: 9783642261398
Lingua: Inglese
Da: Buchpark, Trebbin, Germania
Condizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.
EUR 177,10
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Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 166,49
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Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 167,67
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EUR 199,89
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Editore: Springer Berlin Heidelberg Nov 2009, 2009
ISBN 10: 3642025315 ISBN 13: 9783642025310
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 106,99
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Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters. This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks. 96 pp. Englisch.
Editore: Springer Berlin Heidelberg Mrz 2012, 2012
ISBN 10: 3642261396 ISBN 13: 9783642261398
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 106,99
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters. This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks. 96 pp. Englisch.
Editore: Springer Berlin Heidelberg, 2012
ISBN 10: 3642261396 ISBN 13: 9783642261398
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 92,27
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensiti.
Editore: Springer Berlin Heidelberg, 2009
ISBN 10: 3642025315 ISBN 13: 9783642025310
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 92,27
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensiti.
Da: Majestic Books, Hounslow, Regno Unito
EUR 147,45
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Aggiungi al carrelloCondizione: New. Print on Demand pp. 96 24 Illus.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 152,06
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. 96.
Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Mär 2012, 2012
ISBN 10: 3642261396 ISBN 13: 9783642261398
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 106,99
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters.This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 96 pp. Englisch.