Da: GreatBookPrices, Columbia, MD, U.S.A.
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 32,09
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Lingua: Inglese
Editore: Springer Nature Switzerland AG, CH, 2022
ISBN 10: 3030958620 ISBN 13: 9783030958626
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 34,47
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Aggiungi al carrelloPaperback. Condizione: New. 1st ed. 2022. This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors' reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods.The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science.This is an open access book.
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EUR 27,49
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 26,05
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Da: California Books, Miami, FL, U.S.A.
EUR 55,65
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Condizione: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
Lingua: Inglese
Editore: Springer Nature Switzerland AG, CH, 2022
ISBN 10: 3030958620 ISBN 13: 9783030958626
Da: Rarewaves.com UK, London, Regno Unito
EUR 28,69
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Aggiungi al carrelloPaperback. Condizione: New. 1st ed. 2022. This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors' reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods.The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science.This is an open access book.
Da: Majestic Books, Hounslow, Regno Unito
EUR 65,12
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 65,21
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.
Lingua: Inglese
Editore: Springer International Publishing, 2022
ISBN 10: 3030958620 ISBN 13: 9783030958626
Da: moluna, Greven, Germania
EUR 39,60
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Powerful tools lead to new principles and algorithms for various linear and nonlinear system identification techniquesCareful mathematics provide a rigorous basis for cross-fertilization between system identification and machine learningDev.
Da: Majestic Books, Hounslow, Regno Unito
EUR 86,78
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 85,56
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Lingua: Inglese
Editore: Springer International Publishing, 2022
ISBN 10: 3030958590 ISBN 13: 9783030958596
Da: moluna, Greven, Germania
EUR 48,37
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Powerful tools lead to new principles and algorithms for various linear and nonlinear system identification techniquesCareful mathematics provide a rigorous basis for cross-fertilization between system identification and machine learningDev.