A Unified Approach To Active Dual Supervision For Labeling Features And Examples
ECML PKDD'10: Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I(2010)
摘要
When faced with the task of building accurate classifiers, active learning is often a beneficial tool for minimizing the requisite costs of human annotation. Traditional active learning schemes query a human for labels on intelligently chosen examples. However, human effort can also be expended in collecting alternative forms of annotation. For example, one may attempt to learn a text classifier by labeling words associated with a class, instead of, or in addition to, documents. Learning from two different kinds of supervision adds a challenging dimension to the problem of active learning. In this paper, we present a unified approach to such active dual supervision: determining which feature or example a classifier is most likely to benefit from having labeled. Empirical results confirm that appropriately querying for both example and feature labels significantly reduces overall human effort-beyond what is possible through traditional one-dimensional active learning.
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关键词
active learning,active dual supervision,traditional active learning scheme,traditional one-dimensional active learning,human annotation,human effort,overall human effort,accurate classifier,feature label,text classifier,unified approach
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