Multiclass Learning

Multiclass learning focuses on classifying data into more than two categories, aiming to develop accurate and efficient classification models. Current research emphasizes improving model performance under challenging conditions, such as noisy labels and imbalanced datasets, exploring algorithms like those based on auction dynamics and Frank-Wolfe methods, and investigating the theoretical foundations of learning rates and consistency for various performance metrics. These advancements have significant implications for diverse applications, including image recognition, natural language processing, and medical diagnosis, by enabling more robust and reliable multiclass classification systems.