Machine Learning and Data Classification

Machine Learning and Data Classification is a research topic within Artificial Intelligence. Science Explorer counts 19k research works in it since 1953. 9.9% of them reached the world's top 10% most cited for their field and year.

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.

• Noisy Labels
• Hyperparameter Optimization
• Instance Selection
• Robust Learning
• Automated Machine Learning
• Meta-Learning
• Deep Neural Networks
• Classification
• Positive and Unlabeled Data
• Loss Correction