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.
EL-MIATTs: Evaluation and Learning with Multiple Inaccurate True Targets
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2roles
background 2representative 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
-
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.
-
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.