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Found 2 papers, 2 papers with code
Robust Loss Functions for Training Decision Trees with Noisy Labels
We consider training decision trees using noisily labeled data, focusing on loss functions that can lead to robust learning algorithms.
Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization
Key to our approach is a new interpretation of decision tree algorithms for positive and negative data as \emph{recursive greedy risk minimization algorithms}.
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