By adopting a negative ontology where the true target does not objectively exist, the paper defines Democratic Supervision and derives the EL-MIATTs framework for ML evaluation and learning with Multiple Inaccurate True Targets.
Supervised learning in DNA neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Simulations identify a bounded regime where embedded DNA inference reporting improves detection of weak-to-moderate anomalies while remaining competitive in communication cost, though it adds delay and does not outperform simpler methods globally.
citing papers explorer
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Negative Ontology of True Target for Machine Learning: Towards Evaluation and Learning under Democratic Supervision
By adopting a negative ontology where the true target does not objectively exist, the paper defines Democratic Supervision and derives the EL-MIATTs framework for ML evaluation and learning with Multiple Inaccurate True Targets.
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Embedded DNA Inference in In-Body Nanonetworks: Detection, Delay, and Communication Trade-Offs
Simulations identify a bounded regime where embedded DNA inference reporting improves detection of weak-to-moderate anomalies while remaining competitive in communication cost, though it adds delay and does not outperform simpler methods globally.