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Abstract: The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers was developed and tested on a newswire text categorization task. This method, which we call uncertainty sampling, reduced by as much as 500-fold the amount of training data that would have to be... (Update)
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BibTeX entry: (Update)
Lewis, D. D. (1995). A sequential algorithm for training text classifiers: Corrigendum and additional data. SIGIR Forum, 29 (2), 13--19. http://citeseer.ist.psu.edu/lewis94sequential.html More
@inproceedings{ lewis94sequential,
author = "David D. Lewis and William A. Gale",
title = "A sequential algorithm for training text classifiers",
booktitle = "Proceedings of {SIGIR}-94, 17th {ACM} International Conference on Research and Development in Information Retrieval",
publisher = "Springer Verlag, Heidelberg, DE",
address = "Dublin, IE",
editor = "W. Bruce Croft and Cornelis J. van Rijsbergen",
pages = "3--12",
year = "1994",
url = "citeseer.ist.psu.edu/lewis94sequential.html" }
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