Kernel Mode Decomposition and the Programming of Kernels (Surveys and Tutorials in the Applied Mathematical Sciences, 8)
Language: English
Published by Springer, 2021
Series: Book 9 of 9 - Surveys and Tutorials in the Applied Mathematical Sciences
- Softcover
- New
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In English.
Seller Inventory # ria9783030821708_new
- Title
- Kernel Mode Decomposition and the Programming of Kernels (Surveys and Tutorials in the Applied Mathematical Sciences, 8)
- Author
- Owhadi, Houman; Scovel, Clint; Yoo, Gene Ryan
- Publisher
- Springer
- Publication year
- 2021
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 3030821706
- ISBN 13
- 9783030821708
- Item weight
- 332 grams
- Series
- Book 9 of 9: Surveys and Tutorials in the Applied Mathematical Sciences
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 context of 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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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 context of 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.
"About the title" may belong to another edition of this title.
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