EAPO adapts wildfire models to new environments via k-nearest neighbor data retrieval and hybrid fine-tuning that emphasizes rare extreme events, achieving ROC-AUC 0.7310 on real data.
K-nearest neighbor.Scholarpedia, 4(2):1883
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
Global color moments and RGB/HSV histograms alone support binary benign-malignant classification at up to 89% accuracy with classical ML classifiers, substantially above random baselines.
Knowledge graph embeddings predict missing connections to generate context-specific data quality assessment plans, tested on a radiation sensor dataset.
citing papers explorer
-
Environment-Adaptive Preference Optimization for Wildfire Prediction
EAPO adapts wildfire models to new environments via k-nearest neighbor data retrieval and hybrid fine-tuning that emphasizes rare extreme events, achieving ROC-AUC 0.7310 on real data.
-
Beyond Morphology: Quantifying the Diagnostic Power of Color Features in Cancer Classification
Global color moments and RGB/HSV histograms alone support binary benign-malignant classification at up to 89% accuracy with classical ML classifiers, substantially above random baselines.
-
Automated Big Data Quality Assessment using Knowledge Graph Embeddings
Knowledge graph embeddings predict missing connections to generate context-specific data quality assessment plans, tested on a radiation sensor dataset.