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Harmonic and applied analysisfrom ra...

De Mari, Filippo.

Harmonic and applied analysisfrom radon transforms to machine learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Harmonic and applied analysisedited by Filippo De Mari, Ernesto De Vito.
Reminder of title:	from radon transforms to machine learning /
other author:	De Mari, Filippo.
Published:	Cham :Springer International Publishing :2021.
Description:	xv, 302 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Harmonic analysis.
Online resource:	https://doi.org/10.1007/978-3-030-86664-8
ISBN:	9783030866648$q(electronic bk.)

Harmonic and applied analysisfrom radon transforms to machine learning /
Harmonic and applied analysisfrom radon transforms to machine learning /[electronic resource] :edited by Filippo De Mari, Ernesto De Vito. - Cham :Springer International Publishing :2021. - xv, 302 p. :ill. (some col.), digital ;24 cm. - Applied and numerical harmonic analysis,2296-5017. - Applied and numerical harmonic analysis..

Bartolucci, F., De Mari, F., Monti, M., Unitarization of the Horocyclic Radon Transform on Symmetric Spaces -- Maurer, A., Entropy and Concentration -- Alaifari, R., Ill-Posed Problems: From Linear to Non-Linear and Beyond -- Salzo, S., Villa, S., Proximal Gradient Methods for Machine Learning and Imaging -- De Vito, E., Rosasco, L., Rudi, A., Regularization: From Inverse Problems to Large Scale Machine Learning.

Deep connections exist between harmonic and applied analysis and the diverse yet connected topics of machine learning, data analysis, and imaging science. This volume explores these rapidly growing areas and features contributions presented at the second and third editions of the Summer Schools on Applied Harmonic Analysis, held at the University of Genova in 2017 and 2019. Each chapter offers an introduction to essential material and then demonstrates connections to more advanced research, with the aim of providing an accessible entrance for students and researchers. Topics covered include ill-posed problems; concentration inequalities; regularization and large-scale machine learning; unitarization of the radon transform on symmetric spaces; and proximal gradient methods for machine learning and imaging.

ISBN: 9783030866648$q(electronic bk.)

Standard No.: 10.1007/978-3-030-86664-8doiSubjects--Topical Terms:

189705
Harmonic analysis.

LC Class. No.: QA403 / .H37 2021

Dewey Class. No.: 515.2433

Harmonic and applied analysisfrom radon transforms to machine learning /
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505 0 $a Bartolucci, F., De Mari, F., Monti, M., Unitarization of the Horocyclic Radon Transform on Symmetric Spaces -- Maurer, A., Entropy and Concentration -- Alaifari, R., Ill-Posed Problems: From Linear to Non-Linear and Beyond -- Salzo, S., Villa, S., Proximal Gradient Methods for Machine Learning and Imaging -- De Vito, E., Rosasco, L., Rudi, A., Regularization: From Inverse Problems to Large Scale Machine Learning.
520 $a Deep connections exist between harmonic and applied analysis and the diverse yet connected topics of machine learning, data analysis, and imaging science. This volume explores these rapidly growing areas and features contributions presented at the second and third editions of the Summer Schools on Applied Harmonic Analysis, held at the University of Genova in 2017 and 2019. Each chapter offers an introduction to essential material and then demonstrates connections to more advanced research, with the aim of providing an accessible entrance for students and researchers. Topics covered include ill-posed problems; concentration inequalities; regularization and large-scale machine learning; unitarization of the radon transform on symmetric spaces; and proximal gradient methods for machine learning and imaging.
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