Machine Learning and Data Classification -- 专题文献

专题：Machine Learning and Data Classification

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.

专题：Machine Learning and Data Classification

最新文献

Dataset Distillation via a Noise-Unconstrained Generative Model

A Survey on Rough Feature Selection: Recent Advances and Challenges

Theory of Random Forests

A comprehensive survey on imbalanced data learning

Representation Learning for Tabular Data: A Comprehensive Survey

Deep transfer learning for image classification: a survey

The impact of K selection in K‑fold cross-validation on bias and variance in supervised learning models

A review of machine learning with small and limited data

Padding interpolation, median imputation, RobustScalar, and particle swarm optimization with heterogeneous classifiers: a robust combination for effective heart disease diagnosis

LIMFS: Label interaction-aware multi-label feature selection

近5年高被引文献