Probabilistic decision trees incorporate uncertainty as noise in three distinct phases, with soft training reducing tree size while preserving accuracy under noise but soft evaluation showing no benefit.
Hierarchical mixtures of experts and the em algorithm,
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Decision Tree Learning for Uncertain Clinical Measurements
Probabilistic decision trees incorporate uncertainty as noise in three distinct phases, with soft training reducing tree size while preserving accuracy under noise but soft evaluation showing no benefit.