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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...
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| Опубликовано в: : | Entropy (Basel) |
|---|---|
| Главные авторы: | , , , , |
| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
MDPI
2019
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| Предметы: | |
| Online-ссылка: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7515258/ https://ncbi.nlm.nih.gov/pubmed/33267443 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/e21080729 |
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