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Confident Learning (CL): Difference between revisions
Created page with "==Introduction== Confident Learning (CL) is a subfield of supervised learning and weak-supervision aimed at characterizing label noise, finding label errors, learning with noisy labels and finding ontological issues. CL is based on the principles of pruning noisy data, counting to estimate noise and ranking examples to train with confidence. CL generalizes Angluin and Laird's classification noise process to directly estimate the joint distribution bet..."
(Created page with "==Introduction== Confident Learning (CL) is a subfield of supervised learning and weak-supervision aimed at characterizing label noise, finding label errors, learning with noisy labels and finding ontological issues. CL is based on the principles of pruning noisy data, counting to estimate noise and ranking examples to train with confidence. CL generalizes Angluin and Laird's classification noise process to directly estimate the joint distribution bet...") |
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