Harmonic and applied analysis: from Radon transforms to machine learning
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 Ima...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham, Switzerland
Birkhäuser
[2021]
Cham Springer International Publishing |
| Schriftenreihe: | Applied and numerical harmonic analysis
|
| Schlagworte: | |
| Zusammenfassung: | 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. |
| Beschreibung: | xv, 302 Seiten Illustrationen |
| ISBN: | 9783030866631 9783030866648 |
Internformat
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| 520 | 3 | |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. | |
| 653 | 0 | |a Harmonic analysis | |
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| dewey-full | 515.785 |
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| discipline | Mathematik |
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| spelling | Harmonic and applied analysis from Radon transforms to machine learning Filippo De Mari ; Ernesto De Vito, editors Cham, Switzerland Birkhäuser [2021] Cham Springer International Publishing © 2021 xv, 302 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier 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. Harmonic analysis Differential geometry Mathematical optimization Data structures (Computer science) Artificial intelligence Signal processing Image processing Speech processing systems (DE-588)4143413-4 Aufsatzsammlung gnd-content De Mari, Filippo 1959- (DE-588)1160336040 edt De Vito, Ernesto (DE-588)1249775906 edt Erscheint auch als Online-Ausgabe 978-3-030-86664-8 |
| spellingShingle | Harmonic and applied analysis from Radon transforms to machine learning |
| subject_GND | (DE-588)4143413-4 |
| title | Harmonic and applied analysis from Radon transforms to machine learning |
| title_auth | Harmonic and applied analysis from Radon transforms to machine learning |
| title_exact_search | Harmonic and applied analysis from Radon transforms to machine learning |
| title_exact_search_txtP | Harmonic and applied analysis from Radon transforms to machine learning |
| title_full | Harmonic and applied analysis from Radon transforms to machine learning Filippo De Mari ; Ernesto De Vito, editors |
| title_fullStr | Harmonic and applied analysis from Radon transforms to machine learning Filippo De Mari ; Ernesto De Vito, editors |
| title_full_unstemmed | Harmonic and applied analysis from Radon transforms to machine learning Filippo De Mari ; Ernesto De Vito, editors |
| title_short | Harmonic and applied analysis |
| title_sort | harmonic and applied analysis from radon transforms to machine learning |
| title_sub | from Radon transforms to machine learning |
| topic_facet | Aufsatzsammlung |
| work_keys_str_mv | AT demarifilippo harmonicandappliedanalysisfromradontransformstomachinelearning AT devitoernesto harmonicandappliedanalysisfromradontransformstomachinelearning |