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New BOLT loss function aims to improve classifier training accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced BOLT, a novel training loss function designed to provide a universal upper bound on the Bayes error rate for classifiers. This new loss function, derived from the Bayes error rate, aims to guide parameter updates during training by minimizing this bound. Experiments on synthetic and real-world datasets suggest that BOLT training can achieve lower held-out classification error compared to traditional cross-entropy training. AI

IMPACT BOLT training may offer an alternative to cross-entropy, potentially leading to more accurate classifiers in specific applications.

RANK_REASON The cluster contains a research paper detailing a new method for classifier training. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New BOLT loss function aims to improve classifier training accuracy

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The cluster contains a research paper detailing a new method for classifier training. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mohammadreza Tavasoli Naeini, Ali Bereyhi, Morteza Noshad, Ben Liang, Alfred O. Hero III ·

    BOLT: Universal Classifier Training via a Bayes-Error Upper Bound

    arXiv:2501.07754v2 Announce Type: replace Abstract: This work introduces a novel approach to compute a universal upper bound on the Bayes error rate (BER) from classifier-output samples, which serves as a Bayes-error-related performance criterion for the underlying classification…

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