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Da: Ria Christie Collections, Uxbridge, Regno Unito
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Condizione: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.
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Aggiungi al carrelloPaperback. Condizione: Brand New. 128 pages. 9.25x6.10x0.28 inches. In Stock.
Lingua: Inglese
Editore: Springer, Berlin|Springer International Publishing|Springer, 2021
ISBN 10: 3030821706 ISBN 13: 9783030821708
Da: moluna, Greven, Germania
EUR 78,56
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Aggiungi al carrelloCondizione: New. This monograph demonstrates a new approach to the classical mode decomposition problem through nonlinear regression models, which achieve near-machine precision in the recovery of the modes.This monograph demonstrates a new approach to the classical .
Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing, 2021
ISBN 10: 3030821706 ISBN 13: 9783030821708
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 80,24
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This monograph demonstrates a new approach to the classical mode decomposition problem through nonlinear regression models, which achieve near-machine precision in the recovery of the modes. The presentation includes a review of generalized additive models, additive kernels/Gaussian processes, generalized Tikhonov regularization, empirical mode decomposition, and Synchrosqueezing, which are all related to and generalizable under the proposed framework.Although kernel methods have strong theoretical foundations, they require the prior selection of a good kernel. While the usual approach to this kernel selection problem is hyperparameter tuning, the objective of this monograph is to present an alternative (programming) approach to the kernel selection problem while using mode decomposition as a prototypical pattern recognition problem. In this approach, kernels are programmed for the task at hand through the programming of interpretable regression networks in the contextof additive Gaussian processes.It is suitable for engineers, computer scientists, mathematicians, and students in these fields working on kernel methods, pattern recognition, and mode decomposition problems.
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Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer International Publishing Dez 2021, 2021
ISBN 10: 3030821706 ISBN 13: 9783030821708
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 80,24
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This monograph demonstrates a new approach to the classical mode decomposition problem through nonlinear regression models, which achieve near-machine precision in the recovery of the modes. The presentation includes a review of generalized additive models, additive kernels/Gaussian processes, generalized Tikhonov regularization, empirical mode decomposition, and Synchrosqueezing, which are all related to and generalizable under the proposed framework.Although kernel methods have strong theoretical foundations, they require the prior selection of a good kernel. While the usual approach to this kernel selection problem is hyperparameter tuning, the objective of this monograph is to present an alternative (programming) approach to the kernel selection problem while using mode decomposition as a prototypical pattern recognition problem. In this approach, kernels are programmed for the task at hand through the programming of interpretable regression networks in the contextof additive Gaussian processes.It is suitable for engineers, computer scientists, mathematicians, and students in these fields working on kernel methods, pattern recognition, and mode decomposition problems. 128 pp. Englisch.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 103,29
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Da: Majestic Books, Hounslow, Regno Unito
EUR 110,05
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Aggiungi al carrelloCondizione: New. Print on Demand.
Lingua: Inglese
Editore: Springer, Palgrave Macmillan Dez 2021, 2021
ISBN 10: 3030821706 ISBN 13: 9783030821708
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 80,24
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This monograph demonstrates a new approach to the classical mode decomposition problem through nonlinear regression models, which achieve near-machine precision in the recovery of the modes. The presentation includes a review of generalized additive models, additive kernels/Gaussian processes, generalized Tikhonov regularization, empirical mode decomposition, and Synchrosqueezing, which are all related to and generalizable under the proposed framework.Although kernel methods have strong theoretical foundations, they require the prior selection of a good kernel. While the usual approach to this kernel selection problem is hyperparameter tuning, the objective of this monograph is to present an alternative (programming) approach to the kernel selection problem while using mode decomposition as a prototypical pattern recognition problem. In this approach, kernels are programmed for the task at hand through the programming of interpretable regression networks in the contextof additive Gaussian processes.It is suitable for engineers, computer scientists, mathematicians, and students in these fields working on kernel methods, pattern recognition, and mode decomposition problems.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 128 pp. Englisch.