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Universal Target Learning: An Efficient and Effective Technique for Semi-Naive Bayesian Learning
2019
Entropy
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 unlabeled testing instance P the Bayesian Network Classifier BNC P , which is independent and complementary to BNC T learned from training data T . In this paper, we extend TL to Universal Target Learning
doi:10.3390/e21080729
pmid:33267443
pmcid:PMC7515258
fatcat:qxdhbv2yorf63fnhqgkpoffbv4