03543nam 22005655 450 991052008560332120260812170820.03-030-82171-410.1007/978-3-030-82171-5(MiAaPQ)EBC6838762(Au-PeEL)EBL6838762(CKB)20275205600041(OCoLC)1292360497(PPN)259386367(DE-He213)978-3-030-82171-5(EXLCZ)992027520560004120211203d2021 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierKernel Mode Decomposition and the Programming of Kernels /by Houman Owhadi, Clint Scovel, Gene Ryan Yoo1st ed. 2021.Cham :Springer International Publishing :Imprint: Springer,2021.1 online resource (125 pages)Surveys and Tutorials in the Applied Mathematical Sciences,2199-4773 ;8Print version: Owhadi, Houman Kernel Mode Decomposition and the Programming of Kernels Cham : Springer International Publishing AG,c2022 9783030821708 Introduction -- Review -- The mode decomposition problem -- Kernel mode decomposition networks (KMDNets) -- Additional programming modules and squeezing -- Non-trigonometric waveform and iterated KMD -- Unknown base waveforms -- Crossing frequencies, vanishing modes, and noise -- Appendix.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.Surveys and Tutorials in the Applied Mathematical Sciences,2199-4773 ;8Neural networks (Computer science)Approximation theoryMathematical Models of Cognitive Processes and Neural NetworksApproximations and ExpansionsNeural networks (Computer science)Approximation theory.Mathematical Models of Cognitive Processes and Neural Networks.Approximations and Expansions.518.2Owhadi Houman1074716Scovel Clint1955-Yoo Gene RyanMiAaPQMiAaPQMiAaPQBOOK9910520085603321Kernel mode decomposition and the programming of Kernels2909943UNINA