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Abstract: the largest e ort to date to evaluate explicit objective function methods in active learning. We employ seven data sets in the evaluation from domains such as image recognition and document classi cation. The data sets vary in number of categories from 2 to 26 and have as many as 6; 191 predictors. Our work establishes the bene ts of these often cited (but rarely used) strategies, and counters the claim that experimental design methods are too computationally complex to run on interesting... (Update)
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BibTeX entry: (Update)
@misc{ schein-active,
author = "Andrew I. Schein and Lyle H. Ungar",
title = "Active Learning for Multi-Class Logistic Regression",
url = "citeseer.ist.psu.edu/article/schein05active.html" }
Citations (may not include all citations):105 Information-based objective functions for active data select.. - MacKay - 1992 ACM
77 Neural network exploration using optimal experimental design - Cohn - 1996
19 Toward optimal active learning through sampling estimation o.. - Roy, McCallum - 2001 ACM DBLP
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