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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 “...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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MDPI
2021
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8619508/ https://www.ncbi.nlm.nih.gov/pubmed/34828227 http://dx.doi.org/10.3390/e23111529 |