A Sequential Algorithm for Training
نویسنده
چکیده
The ability to cheaply train text classiiers 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 classiiers 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 manually classiied to achieve a given level of eeectiveness.
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تاریخ انتشار 1994