Multi-Class Active Learning by Integrating Uncertainty and Diversity
Active learning is a promising way to reduce the labeling cost with a limited training samples initially, and then iteratively select the most valuable samples from a large number of unlabeled data for labeling in order to construct a powerful classifier.The goal of active learning is to make the labeled data set has no redundancy as much as possib