Still No Free Lunches: The Price to Pay for Tighter PAC-Bayes Bounds
“No free lunch” results state the impossibility of obtaining meaningful bounds on the error of a learning algorithm without prior assumptions and modelling, which is more or less realistic for a given problem. Some models are “expensive” (strong assumptions, such as sub-Gaussian tails), others are “...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Acceso en línea: | https://doaj.org/article/60e63bead773480ead8e98baa4144956 |
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