Multiparameter regularization and aggregation in the context of polynomial functional regression
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911201982676992 |
|---|---|
| author | Gizewski, Elke R. Holzleitner, Markus Mayer-Suess, Lukas Pereverzyev Jr., Sergiy Pereverzyev, Sergei V. |
| author_facet | Gizewski, Elke R. Holzleitner, Markus Mayer-Suess, Lukas Pereverzyev Jr., Sergiy Pereverzyev, Sergei V. |
| contents | Most of the recent results in polynomial functional regression have been focused on an in-depth exploration of single-parameter regularization schemes. In contrast, in this study we go beyond that framework by introducing an algorithm for multiple parameter regularization and presenting a theoretically grounded method for dealing with the associated parameters. This method facilitates the aggregation of models with varying regularization parameters. The efficacy of the proposed approach is assessed through evaluations on both synthetic and some real-world medical data, revealing promising results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04147 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multiparameter regularization and aggregation in the context of polynomial functional regression Gizewski, Elke R. Holzleitner, Markus Mayer-Suess, Lukas Pereverzyev Jr., Sergiy Pereverzyev, Sergei V. Machine Learning Numerical Analysis Statistics Theory 65K10, 62G20 Most of the recent results in polynomial functional regression have been focused on an in-depth exploration of single-parameter regularization schemes. In contrast, in this study we go beyond that framework by introducing an algorithm for multiple parameter regularization and presenting a theoretically grounded method for dealing with the associated parameters. This method facilitates the aggregation of models with varying regularization parameters. The efficacy of the proposed approach is assessed through evaluations on both synthetic and some real-world medical data, revealing promising results. |
| title | Multiparameter regularization and aggregation in the context of polynomial functional regression |
| topic | Machine Learning Numerical Analysis Statistics Theory 65K10, 62G20 |
| url | https://arxiv.org/abs/2405.04147 |