Risk-Calibrated Learning reduces critical error rates in medical AI by 20-92% across four imaging datasets by embedding a severity matrix into the optimization.
The foundations of cost-sensitive learning
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Online adaptations of early time series classifiers, particularly RL-based ones, improve robustness to drifting or stochastic decision costs.
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Risk-Calibrated Learning: Minimizing Fatal Errors in Medical AI
Risk-Calibrated Learning reduces critical error rates in medical AI by 20-92% across four imaging datasets by embedding a severity matrix into the optimization.
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Early Classification of Time Series in Non-Stationary Cost Regimes
Online adaptations of early time series classifiers, particularly RL-based ones, improve robustness to drifting or stochastic decision costs.