Machine Learning and Data Classification
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Scope of this subject
This cluster of papers focuses on the challenges and techniques for learning with noisy labels in machine learning, including methods for hyperparameter optimization, instance selection, robust learning, and automated machine learning. It also explores the use of meta-learning and deep neural networks in handling noisy label problems, particularly in the context of classification tasks and learning from positive and unlabeled data.
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Research vocabulary
label noise; automated machine learning; instance selection; ensemble diversity; classifier diversity; positive-unlabeled learning; label noise robustness; partial label learning; meta-features; ensemble pruning; error-correcting output codes; noisy label learning; label correction; ensemble selection; label disambiguation; ensemble accuracy; coreset selection; Hyperband; mislabeled data; label noise detection; dynamic ensemble selection; class noise; label noise correction; learning with noisy labels; noisy label detection
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Numeric address and provenance
Morton address: 38285273125752027 · morton4x16-v1
Coordinates: (3, 17, 1702, 12535). The address interleaves categorical identifier bits. Nearby addresses are not guaranteed to describe semantically similar subjects.
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