PAC-Bayes generalization bounds offer a theoretical foundation for learning classifiers with low generalization error and predicting their performance on unseen data. Current formulations implicitly assume that the relative cost of misclassifying a positive or negative example is reflected by the class skew in the training dataset. We present a learning approach based on minimizing an asymmetric generalization bound that enables PAC-Bayesian model selection under a class-specific performance constraint.<\/p>\n","protected":false},"excerpt":{"rendered":"
PAC-Bayes generalization bounds offer a theoretical foundation for learning classifiers with low generalization error and predicting their performance on unseen data. Current formulations implicitly assume that the relative cost of misclassifying a positive or negative example is reflected by the class skew in the training dataset. We present a learning approach based on minimizing an 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