On regularized polynomial functional regression
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866914786443264000 |
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| author | Holzleitner, Markus Pereverzyev, Sergei |
| author_facet | Holzleitner, Markus Pereverzyev, Sergei |
| contents | This article offers a comprehensive treatment of polynomial functional regression, culminating in the establishment of a novel finite sample bound. This bound encompasses various aspects, including general smoothness conditions, capacity conditions, and regularization techniques. In doing so, it extends and generalizes several findings from the context of linear functional regression as well. We also provide numerical evidence that using higher order polynomial terms can lead to an improved performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_03036 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | On regularized polynomial functional regression Holzleitner, Markus Pereverzyev, Sergei Numerical Analysis Machine Learning Statistics Theory 65K10, 62G20, 62J05 This article offers a comprehensive treatment of polynomial functional regression, culminating in the establishment of a novel finite sample bound. This bound encompasses various aspects, including general smoothness conditions, capacity conditions, and regularization techniques. In doing so, it extends and generalizes several findings from the context of linear functional regression as well. We also provide numerical evidence that using higher order polynomial terms can lead to an improved performance. |
| title | On regularized polynomial functional regression |
| topic | Numerical Analysis Machine Learning Statistics Theory 65K10, 62G20, 62J05 |
| url | https://arxiv.org/abs/2311.03036 |