Learning functions, operators and dynamical systems with kernels
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909801727918080 |
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| author | Rosasco, Lorenzo |
| author_facet | Rosasco, Lorenzo |
| contents | This expository article presents the approach to statistical machine learning based on reproducing kernel Hilbert spaces. The basic framework is introduced for scalar-valued learning and then extended to operator learning. Finally, learning dynamical systems is formulated as a suitable operator learning problem, leveraging Koopman operator theory. The manuscript collects the supporting material for the corresponding course taught at the CIME school "Machine Learning: From Data to Mathematical Understanding" in Cetraro. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18071 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning functions, operators and dynamical systems with kernels Rosasco, Lorenzo Machine Learning This expository article presents the approach to statistical machine learning based on reproducing kernel Hilbert spaces. The basic framework is introduced for scalar-valued learning and then extended to operator learning. Finally, learning dynamical systems is formulated as a suitable operator learning problem, leveraging Koopman operator theory. The manuscript collects the supporting material for the corresponding course taught at the CIME school "Machine Learning: From Data to Mathematical Understanding" in Cetraro. |
| title | Learning functions, operators and dynamical systems with kernels |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.18071 |