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Universal Target Learning: An Efficient and Effective Technique for Semi-Naive Bayesian Learning
To mitigate the negative effect of classification bias caused by overfitting, semi-naive Bayesian techniques seek to mine the implicit dependency relationships in unlabeled testing instances. By redefining some criteria from information theory, Target Learning (TL) proposes to build for each unlabel...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2019
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515258/ https://www.ncbi.nlm.nih.gov/pubmed/33267443 http://dx.doi.org/10.3390/e21080729 |