Proposes the EL-MIATTs framework for ML predictive modeling by assuming the true target does not exist and defining democratic supervision via multiple inaccurate true targets.
Logical assessment formula and its principles for evaluations with inaccurate ground-truth labels
2 Pith papers cite this work. Polarity classification is still indexing.
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citation-polarity summary
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
unclear 1representative citing papers
The EL-MIATTs framework offers LAF-grounded evaluation and UTTL-grounded learning strategies to handle multiple inaccurate true targets in machine learning under uncertain supervision.
citing papers explorer
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Negative Ontology of True Target for Machine Learning: Towards Evaluation and Learning under Democratic Supervision
Proposes the EL-MIATTs framework for ML predictive modeling by assuming the true target does not exist and defining democratic supervision via multiple inaccurate true targets.
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LAF-Based Evaluation and UTTL-Based Learning Strategies with MIATTs
The EL-MIATTs framework offers LAF-grounded evaluation and UTTL-grounded learning strategies to handle multiple inaccurate true targets in machine learning under uncertain supervision.