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Information-Corrected Estimation: A Generalization Error Reducing Parameter Estimation Method
Modern computational models in supervised machine learning are often highly parameterized universal approximators. As such, the value of the parameters is unimportant, and only the out of sample performance is considered. On the other hand much of the literature on model estimation assumes that the...
| 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/PMC8621511/ https://www.ncbi.nlm.nih.gov/pubmed/34828117 http://dx.doi.org/10.3390/e23111419 |