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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Detalles Bibliográficos
Autores principales: Gao, Siqi, Lou, Hua, Wang, Limin, Liu, Yang, Fan, Tiehu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
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

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515258/
https://www.ncbi.nlm.nih.gov/pubmed/33267443
http://dx.doi.org/10.3390/e21080729

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