Learning with Noisy Labels in Machine Learning (Q169072): Difference between revisions

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description / endescription / en
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
"Analyzing machine learning models that learn with incorrect or missing data labels."

Revision as of 14:51, 30 August 2024

"Analyzing machine learning models that learn with incorrect or missing data labels."
  • Noisy Labels
  • Hyperparameter Optimization
  • Instance Selection
  • Robust Learning
  • Automated Machine Learning
  • Meta-Learning
  • Deep Neural Networks
  • Classification
  • Positive and Unlabeled Data
  • Loss Correction
Language Label Description Also known as
English
Learning with Noisy Labels in Machine Learning
"Analyzing machine learning models that learn with incorrect or missing data labels."
  • Noisy Labels
  • Hyperparameter Optimization
  • Instance Selection
  • Robust Learning
  • Automated Machine Learning
  • Meta-Learning
  • Deep Neural Networks
  • Classification
  • Positive and Unlabeled Data
  • Loss Correction

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