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Method evidence record

Active Learning Logistic Regression

Active Learning with Logistic Regression is an iterative label-efficient framework in which a logistic regression model selects the unlabeled examples it is most uncertain about, an oracle (human annotator) labels them, and the model is retrained — repeating until a labeling budget or accuracy target is met. It dramatically reduces annotation cost compared to random labeling.

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Active Learning with Logistic Regression (Uncertainty Sampling)

Taxonomic method recordml-modelmachine-learning

  1. Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison. · URL

  2. Lewis, D. D., & Gale, W. A. (1994). A sequential algorithm for training text classifiers. Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 3–12. · DOI 10.1007/978-1-4471-2099-5_1

Open full method2 recorded citations, copied from the method source record.

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