Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 26,71
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 33,19
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 26,10
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Da: Chiron Media, Wallingford, Regno Unito
EUR 22,94
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 25,87
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 28,86
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 47,45
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Da: Best Price, Torrance, CA, U.S.A.
EUR 43,67
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Da: BargainBookStores, Grand Rapids, MI, U.S.A.
EUR 53,11
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Aggiungi al carrelloPaperback or Softback. Condizione: New. Regularized System Identification: Learning Dynamic Models from Data 1.24. Book.
Da: California Books, Miami, FL, U.S.A.
EUR 54,11
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Da: Books Puddle, New York, NY, U.S.A.
EUR 75,13
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Editore: Springer International Publishing, Springer International Publishing, 2022
ISBN 10: 3030958620 ISBN 13: 9783030958626
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 42,79
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 soRegularized System Identificationwill 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.
Editore: Springer International Publishing, 2022
ISBN 10: 3030958590 ISBN 13: 9783030958596
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 53,49
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Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 soRegularized System Identificationwill 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 76,70
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 79,87
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