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Found 4 papers, 3 papers with code
Single Shot Active Learning using Pseudo Annotators
These pseudo annotators always provide uniform and random labels whenever new unlabeled samples are queried.
A Variance Maximization Criterion for Active Learning
We propose a novel approach which we refer to as maximizing variance for active learning or MVAL for short.
Active Learning Using Uncertainty Information
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 classification model, and evaluate the criteria that we base our selection on.
A Benchmark and Comparison of Active Learning for Logistic Regression
Logistic regression is by far the most widely used classifier in real-world applications.
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