What's new
Warez.Ge

This is a sample guest message. Register a free account today to become a member! Once signed in, you'll be able to participate on this site by adding your own topics and posts, as well as connect with other members through your own private inbox!

Kernel Mode Decomposition and the Programming of Kernels

voska89

Moderator
Staff member
e234d8e76dad7af3ef3e4ea5abf7f577.jpeg

Kernel Mode Decomposition and the Programming of Kernels by Houman Owhadi, Clint Scovel, Gene Ryan Yoo
English | PDF,EPUB | 2021 | 125 Pages | ISBN : 3030821706 | 19.8 MB
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.​

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.


Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live
Links are Interchangeable - No Password - Single Extraction
 

Users who are viewing this thread

Back
Top