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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: 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: 3030958590ISBN 13: 9783030958596
Da: GF Books, Inc., Hawthorne, CA, U.S.A.
Libro
Condizione: 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. 1.86.
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, 2022
ISBN 10: 3030958590ISBN 13: 9783030958596
Da: GF Books, Inc., Hawthorne, CA, U.S.A.
Libro
Condizione: Fine. Book is in Used-LikeNew 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. 1.86.
Editore: Springer, 2022
ISBN 10: 3030958590ISBN 13: 9783030958596
Da: GF Books, Inc., Hawthorne, CA, U.S.A.
Libro
Condizione: 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. 1.86.
Editore: Springer, 2022
ISBN 10: 3030958590ISBN 13: 9783030958596
Da: Book Deals, Tucson, AZ, U.S.A.
Libro
Condizione: Fine. Like New condition. Great condition, but not exactly fully crisp. The book may have been opened and read, but there are no defects to the book, jacket or pages. 1.86.
Editore: Springer, 2022
ISBN 10: 3030958590ISBN 13: 9783030958596
Da: Book Deals, Tucson, AZ, U.S.A.
Libro
Condizione: New. New! This book is in the same immaculate condition as when it was published 1.86.
Editore: Springer, 2022
ISBN 10: 3030958590ISBN 13: 9783030958596
Da: Book Deals, Tucson, AZ, U.S.A.
Libro
Condizione: Very Good. Very Good condition. Shows only minor signs of wear, and very minimal markings inside (if any). 1.86.
Editore: Springer, 2022
ISBN 10: 3030958590ISBN 13: 9783030958596
Da: Books Unplugged, Amherst, NY, U.S.A.
Libro
Condizione: Fair. Buy with confidence! Book is in acceptable condition with wear to the pages, binding, and some marks within 1.86.
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 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.