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Harmonic and Applied Analysis From Radon Transforms to Machine Learning 1st Edition Filippo De Mari Ernesto De Vito Editors

Name: Harmonic and Applied Analysis From Radon Transforms to Machine Learning 1st Edition Filippo De Mari Ernesto De Vito Editors
SKU: EB-36876848
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Harmonic and Applied Analysis From Radon Transforms to Machine Learning 1st Edition Filippo De Mari Ernesto De Vito Editors Digital Instant Download

Author(s): Filippo De Mari Ernesto De Vito Editors

Edition: 1

File Details: PDF, 4.12 MB

Year: 2021

Language: english

SKU: EB-36876848 Category: Mathematics - Analysis Tags: 3030866637, 9783030866631, Applied Analysis, Filippo De Mari Ernesto De Vito Editors, Harmonic, Machine Learning, Radon Transforms

Description

Harmonic and Applied Analysis From Radon Transforms to Machine Learning 1st Edition Filippo De Mari Ernesto De Vito Editors – Ebook PDF Instant Download/Delivery ISBN: 9783030866631, 3030866637

Product details:

• ISBN 10:3030866637
• ISBN 13:9783030866631
• Author(s): Filippo De Mari Ernesto De Vito Editors

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.

Table contents:

Unitarization of the Horocyclic Radon Transform on Symmetric Spaces
Entropy and Concentration
Ill-Posed Problems: From Linear to Nonlinear and Beyond
Proximal Gradient Methods for Machine Learning and Imaging
Regularization: From Inverse Problems to Large-Scale Machine Learning

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