K-fold CUBV combines cross-validation with PAC-Bayesian upper bounds on actual risk to provide a more robust criterion for validating ML accuracy and reducing false positives than standard CV.
Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications
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Is K-fold cross validation the best model selection method for Machine Learning?
K-fold CUBV combines cross-validation with PAC-Bayesian upper bounds on actual risk to provide a more robust criterion for validating ML accuracy and reducing false positives than standard CV.