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Editore: Springer, 2022
ISBN 10: 3030958620ISBN 13: 9783030958626
Da: GreatBookPrices, Columbia, MD, U.S.A.
Libro
Condizione: As New. Unread book in perfect condition.
Editore: Esculapio
ISBN 10: 8893851571ISBN 13: 9788893851572
Da: libreriauniversitaria.it, Occhiobello, RO, Italia
Libro
Condizione: NEW.
Editore: Springer, 2022
ISBN 10: 3030958620ISBN 13: 9783030958626
Da: Ria Christie Collections, Uxbridge, Regno Unito
Libro Print on Demand
Condizione: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Editore: Springer, 2022
ISBN 10: 3030958620ISBN 13: 9783030958626
Da: GreatBookPricesUK, Castle Donington, DERBY, Regno Unito
Libro
Condizione: As New. Unread book in perfect condition.
Editore: Società Editrice Esculapio, 2005
ISBN 10: 8893850338ISBN 13: 9788893850339
Da: libreriauniversitaria.it, Occhiobello, RO, Italia
Libro
Condizione: NEW.
Editore: Springer, 2022
ISBN 10: 3030958620ISBN 13: 9783030958626
Da: booksXpress, Bayonne, NJ, U.S.A.
Libro
Soft Cover. Condizione: new.
Editore: Esculapio, 2024
ISBN 10: 8893854341ISBN 13: 9788893854344
Da: libreriauniversitaria.it, Occhiobello, RO, Italia
Libro
Condizione: NEW.
Editore: Springer, 2022
ISBN 10: 3030958620ISBN 13: 9783030958626
Da: GreatBookPrices, Columbia, MD, U.S.A.
Libro
Condizione: New.
Editore: Springer 6/2/2022, 2022
ISBN 10: 3030958620ISBN 13: 9783030958626
Da: BargainBookStores, Grand Rapids, MI, U.S.A.
Libro
Paperback or Softback. Condizione: New. Regularized System Identification: Learning Dynamic Models from Data 1.24. Book.
Editore: Springer, 2022
ISBN 10: 3030958620ISBN 13: 9783030958626
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
Libro
Condizione: New.
Editore: Springer 2022-05, 2022
ISBN 10: 3030958620ISBN 13: 9783030958626
Da: Chiron Media, Wallingford, Regno Unito
Libro
PF. Condizione: New.
Editore: Springer, 2022
ISBN 10: 3030958620ISBN 13: 9783030958626
Da: California Books, Miami, FL, U.S.A.
Libro
Condizione: New.
Editore: Springer, 2022
ISBN 10: 3030958590ISBN 13: 9783030958596
Da: booksXpress, Bayonne, NJ, U.S.A.
Libro
Hardcover. Condizione: new.
Editore: Springer International Publishing Mai 2022, 2022
ISBN 10: 3030958620ISBN 13: 9783030958626
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Libro Print on Demand
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 404 pp. Englisch.
Editore: Springer International Publishing Mai 2022, 2022
ISBN 10: 3030958590ISBN 13: 9783030958596
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Libro Print on Demand
Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 404 pp. Englisch.
Editore: Springer, 2022
ISBN 10: 3030958620ISBN 13: 9783030958626
Da: GreatBookPricesUK, Castle Donington, DERBY, Regno Unito
Libro
Condizione: New.
Editore: Springer International Publishing, 2022
ISBN 10: 3030958620ISBN 13: 9783030958626
Da: AHA-BUCH GmbH, Einbeck, Germania
Libro
Taschenbuch. 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: 3030958620ISBN 13: 9783030958626
Da: moluna, Greven, Germania
Libro Print on Demand
Kartoniert / Broschiert. Condizione: 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.
Editore: Springer International Publishing, 2022
ISBN 10: 3030958590ISBN 13: 9783030958596
Da: AHA-BUCH GmbH, Einbeck, Germania
Libro
Buch. 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, 2022
ISBN 10: 3030958590ISBN 13: 9783030958596
Da: GF Books, Inc., Hawthorne, CA, U.S.A.
Libro
Condizione: New. Book is in NEW condition. 1.86.
Editore: Springer International Publishing, 2022
ISBN 10: 3030958590ISBN 13: 9783030958596
Da: moluna, Greven, Germania
Libro Print on Demand
Gebunden. Condizione: 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.