On the Saturation Effect of Kernel Ridge Regression
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914814625841152 |
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| author | Li, Yicheng Zhang, Haobo Lin, Qian |
| author_facet | Li, Yicheng Zhang, Haobo Lin, Qian |
| contents | The saturation effect refers to the phenomenon that the kernel ridge regression (KRR) fails to achieve the information theoretical lower bound when the smoothness of the underground truth function exceeds certain level. The saturation effect has been widely observed in practices and a saturation lower bound of KRR has been conjectured for decades. In this paper, we provide a proof of this long-standing conjecture. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09362 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the Saturation Effect of Kernel Ridge Regression Li, Yicheng Zhang, Haobo Lin, Qian Machine Learning The saturation effect refers to the phenomenon that the kernel ridge regression (KRR) fails to achieve the information theoretical lower bound when the smoothness of the underground truth function exceeds certain level. The saturation effect has been widely observed in practices and a saturation lower bound of KRR has been conjectured for decades. In this paper, we provide a proof of this long-standing conjecture. |
| title | On the Saturation Effect of Kernel Ridge Regression |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.09362 |