Active Learning (AL) methods seek to improve classifier performance when labels are expensive or scarce. We consider two central questions: Where does AL work? How much does it help? To address these questions, a comprehensive experimental simulation study of Active Learning is presented. We consider a variety of tasks, classifiers and other AL factors, to present a broad exploration of AL performance in various settings. A precise way to quantify performance is needed in order to know when AL works. Thus we also present a detailed methodology for tackling the complexities of assessing AL performance in the context of this experimental study. © 2013 Springer-Verlag.
CITATION STYLE
Evans, L. P. G., Adams, N. M., & Anagnostopoulos, C. (2013). When does active learning work? In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8207 LNCS, pp. 174–185). https://doi.org/10.1007/978-3-642-41398-8_16
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