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• W2760160010 abstract "Active learning has the potential to save costs by intelligent use of resources in form of some expert’s knowledge. Nevertheless, these methods are still not established in real-world applications as they can not be evaluated properly in the specific scenario because evaluation data is missing. In this article, we provide a summary of different evaluation methodologies by discussing them in terms of being reproducible, comparable, and realistic. A pilot study which compares the results of different exhaustive evaluations suggests a lack in repetitions in many articles. Furthermore, we aim to start a discussion on a gold standard evaluation setup for active learning that ensures comparability without reimplementing algorithms." @default.
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• W2760160010 date "2017-09-13" @default.
• W2760160010 modified "2023-09-26" @default.
• W2760160010 title "Challenges of Reliable, Realistic and Comparable Active Learning Evaluation" @default.
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