Classification, as a research area, primarily refers to methods and theory for assigning objects, instances, or signals to discrete categories based on observed features, typically within statistics, machine learning, and pattern recognition. It encompasses the study of supervised learning algorithms (e.g., logistic regression, support vector machines, decision trees, neural networks), loss functions, generalization error, and evaluation metrics such as accuracy, precision, recall, and ROC analysis. Research focuses on consistency, sample complexity, robustness, feature representation, handling class imbalance, and extensions such as multilabel, hierarchical, probabilistic, and open-set classification, often with applications across biology, medicine, engineering, and information retrieval.
Researchers have developed a deep learning-based approach that significantly streamlines the accurate identification and classification of two-dimensional (2D) materials through Raman spectroscopy. In comparison, traditional ...
Nanomaterials
Nov 14, 2024
0
27
Physicists use theoretical models to study physical quantities, such as the mass of nuclei, where they do not have experimental data. However, using a single imperfect theoretical model can lead to misleading results. To ...
General Physics
Mar 11, 2024
0
84
