141 citations · 200 across the 4 of their papers we have counts for
4 papers
Single Shot Active Learning using Pseudo Annotators
Yazhou Yang, Marco Loog
Standard myopic active learning assumes that human annotations are always obtainable whenever new samples are selected. This, however, is unrealistic in many real-world application…
A Variance Maximization Criterion for Active Learning
Yazhou Yang, Marco Loog
Active learning aims to train a classifier as fast as possible with as few labels as possible. The core element in virtually any active learning strategy is the criterion that meas…
Active Learning Using Uncertainty Information
Yazhou Yang, Marco Loog
Many active learning methods belong to the retraining-based approaches, which select one unlabeled instance, add it to the training set with its possible labels, retrain the classi…
A Benchmark and Comparison of Active Learning for Logistic Regression
Yazhou Yang, Marco Loog
Logistic regression is by far the most widely used classifier in real-world applications. In this paper, we benchmark the state-of-the-art active learning methods for logistic regr…