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Titlebook: Kernel Mode Decomposition and the Programming of Kernels; Houman Owhadi,Clint Scovel,Gene Ryan Yoo Book 2021 The Editor(s) (if applicable)

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发表于 2025-3-21 19:31:41 | 显示全部楼层 |阅读模式
书目名称Kernel Mode Decomposition and the Programming of Kernels
编辑Houman Owhadi,Clint Scovel,Gene Ryan Yoo
视频video
概述Introduces programmable and interpretable regression networks for pattern recognition.Uses the classical mode decomposition problem to precisely illustrate models.Demonstrates a program for representi
丛书名称Surveys and Tutorials in the Applied Mathematical Sciences
图书封面Titlebook: Kernel Mode Decomposition and the Programming of Kernels;  Houman Owhadi,Clint Scovel,Gene Ryan Yoo Book 2021 The Editor(s) (if applicable)
描述.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..
出版日期Book 2021
关键词Kernel methods; empirical mode decomposition; Gaussian process regression; additive models; time-frequen
版次1
doihttps://doi.org/10.1007/978-3-030-82171-5
isbn_softcover978-3-030-82170-8
isbn_ebook978-3-030-82171-5Series ISSN 2199-4765 Series E-ISSN 2199-4773
issn_series 2199-4765
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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发表于 2025-3-21 20:14:12 | 显示全部楼层
2199-4765 sely illustrate models.Demonstrates a program for representi.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 generalize
发表于 2025-3-22 04:09:25 | 显示全部楼层
Additional Programming Modules and Squeezing,ions using linear techniques, but can also be thought of as a sparsification technique whose goal is to reduce the computational complexity of solving the corresponding GPR problem, much like the sparse methods have been invented for GPR discussed in Sect. ..
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Book 2021sk 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..
发表于 2025-3-22 13:54:33 | 显示全部楼层
Houman Owhadi,Clint Scovel,Gene Ryan Yooassification, Support Vector Machines, Neural Networks, and Decision Trees; discusses techniques for indexing, image ranking, and image presentation, along with image database visualization methods; provides se978-3-030-17991-5978-3-030-17989-2Series ISSN 1868-0941 Series E-ISSN 1868-095X
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