Overview
- Introduces programmable and interpretable regression networks for pattern recognition
- Uses the classical mode decomposition problem to precisely illustrate models
- Demonstrates a program for representing nonlinearities through hierarchies
Part of the book series: Surveys and Tutorials in the Applied Mathematical Sciences (STAMS, volume 8)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (9 chapters)
-
Front Matter
-
Back Matter
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Kernel Mode Decomposition and the Programming of Kernels
Authors: Houman Owhadi, Clint Scovel, Gene Ryan Yoo
Series Title: Surveys and Tutorials in the Applied Mathematical Sciences
DOI: https://doi.org/10.1007/978-3-030-82171-5
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Softcover ISBN: 978-3-030-82170-8Published: 04 December 2021
eBook ISBN: 978-3-030-82171-5Published: 01 January 2022
Series ISSN: 2199-4765
Series E-ISSN: 2199-4773
Edition Number: 1
Number of Pages: X, 118
Number of Illustrations: 10 b/w illustrations, 31 illustrations in colour
Topics: Mathematical Models of Cognitive Processes and Neural Networks, Approximations and Expansions