Houman owhadi (29 risultati)

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Condizione: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

Paperback. Condizione: Brand New. 128 pages. 9.25x6.10x0.28 inches. In Stock.

Condizione: 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 .

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Soft cover. Condizione: Fair. Fair condition. Clean pages, intact binding.

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Taschenbuch. Condizione: Neu. Kernel Mode Decomposition and the Programming of Kernels | Houman Owhadi (u. a.) | Taschenbuch | Surveys and Tutorials in the Applied Mathematical Sciences | x | Englisch | 2021 | Springer | EAN 9783030821708 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

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Condizione: Very Good. Cham, Switzerland: Springer 2017. Hardcover book. Volume 3. Sm 4to. xxv + 692 pp. Many color and B/W illustrations. Very good book. Volume 3. (mathematics, probabilities, stochastic processes) Inquire if you need further information.

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Taschenbuch. 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.…

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Taschenbuch. 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.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Shares cutting edge Uncertainty Quantification ideas with a wide audienceOverviews fundamental challenges, applications, and emerging results Draws together the work of mathematicians, statisticians, and engineersOpens the work of to. …