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Harmonic and applied analysis : from radon transforms to machine learning / Filippo De Mari, Ernesto De Vito, editors.

De Mari, Filippo, editor.; De Vito, Ernesto, editor.
2021
QA403 .H37 2021
Available Online
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Title
Harmonic and applied analysis : from radon transforms to machine learning / Filippo De Mari, Ernesto De Vito, editors.
ISBN
9783030866648 (electronic bk.)
3030866645 (electronic bk.)
9783030866631
3030866637
DOI
https://doi.org/10.1007/978-3-030-86664-8
Published
Cham : Birkhäuser, [2021]
Copyright
©2021
Language
English
Description
1 online resource : illustrations (some color).
Item Number
10.1007/978-3-030-86664-8 doi
Call Number
QA403 .H37 2021
Dewey Decimal Classification
515/.2433
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed December 28, 2021).
Added Author
De Mari, Filippo, editor.
De Vito, Ernesto, editor.
Series
Applied and numerical harmonic analysis.
Available in Other Form
Print version: 9783030866631
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Online Resources > Ebooks
All Resources
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.

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