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Transfer learning for nonparametric Bayesian networks
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DOI：10.1016/j.knosys.2026.116077.png)
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Abstract
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• Two novel transfer learning methods for nonparametric Bayesian networks. • New metrics proposed to tackle the problem of negative transfer. • A target trust factor reduces source dependence as target data increases. • Cross-domain results show the positive impact of negative transfer metrics. • Statistical evidence of the enhanced performance and reliability of our methods.
Keywords:
Bayesian network
Transfer learning
Kernel density estimation
Nonparametric distribution
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