Items related to Regularized System Identification: Learning Dynamic...
Regularized System Identification: Learning Dynamic Models from Data (Communications and Control Engineering) - Softcover
- PublisherSpringer
- Publication date2022
- ISBN 10 3030958620
- ISBN 13 9783030958626
- BindingPaperback
- LanguageEnglish
- Edition number1
- Number of pages404
Buy New
Learn more about this copy
US$ 39.48
Shipping:
US$ 2.64
Within U.S.A.
Top Search Results from the AbeBooks Marketplace
Seller Image
Regularized System Identification : Learning Dynamic Models from Data
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 4 out of 5 stars
Condition: As New. Unread book in perfect condition. Seller Inventory # 44534898
Quantity: Over 20 available
Seller Image
Regularized System Identification : Learning Dynamic Models from Data
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 4 out of 5 stars
Condition: New. Seller Inventory # 44534898-n
Quantity: Over 20 available
Stock Image
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. Seller Inventory # ria9783030958626_new
Quantity: Over 20 available
Seller Image
Regularized System Identification : Learning Dynamic Models from Data
Seller: GreatBookPricesUK, Castle Donington, DERBY, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 44534898-n
Quantity: Over 20 available
Seller Image
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. Seller Inventory # 44534898
Quantity: Over 20 available
Seller Image
Regularized System Identification: Learning Dynamic Models from Data (Paperback or Softback)
Published by
Springer 6/2/2022, 2022
ISBN 10: 3030958620
ISBN 13: 9783030958626
New
Paperback or Softback
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.
Seller rating 5 out of 5 stars
Paperback or Softback. Condition: New. Regularized System Identification: Learning Dynamic Models from Data 1.24. Book. Seller Inventory # BBS-9783030958626
Quantity: 5 available
Stock Image
Regularized System Identification: Learning Dynamic Models from Data (Communications and Control Engineering)
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # ABLIING23Mar3113020033917
Quantity: Over 20 available
Stock Image
Regularized System Identification: Learning Dynamic Models from Data (Communications and Control Engineering)
Seller: California Books, Miami, FL, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # I-9783030958626
Quantity: Over 20 available
Stock Image
Regularized System Identification : Learning Dynamic Models from Data
Seller: Chiron Media, Wallingford, United Kingdom
Seller rating 5 out of 5 stars
PF. Condition: New. Seller Inventory # 6666-IUK-9783030958626
Quantity: 10 available
Stock Image
Regularized System Identification (Paperback)
Published by
Springer Nature Switzerland AG, Cham, 2022
ISBN 10: 3030958620
ISBN 13: 9783030958626
New
Paperback
First Edition
Seller: Grand Eagle Retail, Wilmington, DE, U.S.A.
Seller rating 5 out of 5 stars
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. Seller Inventory # 9783030958626
Quantity: 1 available