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Condition: As New. Unread book in perfect condition.
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Condition: New.
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Regularized System Identification: Learning Dynamic Models from Data (Communications and Control Engineering)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Regularized System Identification : Learning Dynamic Models from Data
Seller: GreatBookPricesUK, Castle Donington, DERBY, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Regularized System Identification : Learning Dynamic Models from Data
Seller: GreatBookPricesUK, Castle Donington, DERBY, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Paperback or Softback. Condition: New. Regularized System Identification: Learning Dynamic Models from Data 1.24. Book.
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Condition: Good. Buy with confidence! Book is in good condition with minor wear to the pages, binding, and minor marks within 1.34.
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Condition: Very Good. Book is in Used-VeryGood condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain very limited notes and highlighting. 1.34.
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Condition: Good. Book is in Used-Good condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain limited notes and highlighting. 1.34.
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Condition: New. Book is in NEW condition. 1.34.
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Condition: New. New! This book is in the same immaculate condition as when it was published 1.34.
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Esercizi Di Controlli Automatici Con Note Teoriche
Seller: libreriauniversitaria.it, Occhiobello, RO, Italy
Seller rating 4 out of 5 stars
Condition: NEW.
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Regularized System Identification (Paperback)
Published by Springer Nature Switzerland AG, Cham, 2022
ISBN 10: 3030958620 ISBN 13: 9783030958626
Language: English
First Edition
Paperback. Condition: new. Paperback. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
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Regularized System Identification : Learning Dynamic Models from Data
Published by Springer International Publishing, Springer International Publishing, 2022
ISBN 10: 3030958620 ISBN 13: 9783030958626
Language: English
Taschenbuch. Condition: 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.
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Condition: New.
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Regularized System Identification : Learning Dynamic Models from Data
Seller: GreatBookPricesUK, Castle Donington, DERBY, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Regularized System Identification : Learning Dynamic Models from Data
Seller: GreatBookPricesUK, Castle Donington, DERBY, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Regularized System Identification : Learning Dynamic Models from Data
Published by Springer International Publishing, 2022
ISBN 10: 3030958590 ISBN 13: 9783030958596
Language: English
Buch. Condition: 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.
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Sistemi e modelli
Seller: libreriauniversitaria.it, Occhiobello, RO, Italy
Seller rating 4 out of 5 stars
Condition: NEW.
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Sistemi e Modelli (Italian Edition)
Published by Società Editrice Esculapio, 2005
ISBN 10: 8893850338 ISBN 13: 9788893850339
Language: Italian
Seller: libreriauniversitaria.it, Occhiobello, RO, Italy
Seller rating 4 out of 5 stars
Condition: NEW.
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Sistemi e Modelli (Italian Edition)
Published by Società Editrice Esculapio, 2005
ISBN 10: 8893850338 ISBN 13: 9788893850339
Language: Italian
Condition: New. Buy with confidence! Book is in new, never-used condition 1.37.
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Sistemi e Modelli (Italian Edition)
Published by Società Editrice Esculapio, 2005
ISBN 10: 8893850338 ISBN 13: 9788893850339
Language: Italian
Condition: New. New! This book is in the same immaculate condition as when it was published 1.37.
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paperback. Condition: New. Brand New! This item is printed on demand. 1.2400.
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Regularized System Identification
Published by Springer International Publishing Mai 2022, 2022
ISBN 10: 3030958620 ISBN 13: 9783030958626
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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.
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Regularized System Identification
Published by Springer International Publishing Mai 2022, 2022
ISBN 10: 3030958590 ISBN 13: 9783030958596
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: 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.
