C Ost - S Ensitive L Earning via D Eep P Olicy Erm
نویسندگان
چکیده
Deep networks for classification are typically trained by maximizing the log likelihood of the training data. However, the conditional probabilities learned in this way are often not well-calibrated and are thus not well-suited for costsensitive learning where making different errors incurs different rewards or penalties. In this paper, we propose to directly train neural networks to optimize a cost sensitive loss via Empirical Risk Minimization (ERM). Empirical results show that, with proper initialization, ERM training with cost-sensitive loss outperforms maximum-likelihood training with various form of post-processing on a range of cost-sensitive classification tasks.
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