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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.
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.
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
| Erscheinungsjahr: | 2021 |
|---|---|
| Fachbereich: | Allgemeines |
| Genre: | Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
x
118 S. 10 s/w Illustr. 31 farbige Illustr. 118 p. 41 illus. 31 illus. in color. |
| ISBN-13: | 9783030821708 |
| ISBN-10: | 3030821706 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Owhadi, Houman
Yoo, Gene Ryan Scovel, Clint |
| Auflage: | 1st edition 2021 |
| Hersteller: |
Springer Nature Switzerland
Springer International Publishing |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 235 x 155 x 8 mm |
| Von/Mit: | Houman Owhadi (u. a.) |
| Erscheinungsdatum: | 04.12.2021 |
| Gewicht: | 0,207 kg |
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
| Erscheinungsjahr: | 2021 |
|---|---|
| Fachbereich: | Allgemeines |
| Genre: | Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
x
118 S. 10 s/w Illustr. 31 farbige Illustr. 118 p. 41 illus. 31 illus. in color. |
| ISBN-13: | 9783030821708 |
| ISBN-10: | 3030821706 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Owhadi, Houman
Yoo, Gene Ryan Scovel, Clint |
| Auflage: | 1st edition 2021 |
| Hersteller: |
Springer Nature Switzerland
Springer International Publishing |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 235 x 155 x 8 mm |
| Von/Mit: | Houman Owhadi (u. a.) |
| Erscheinungsdatum: | 04.12.2021 |
| Gewicht: | 0,207 kg |
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