Article ID Journal Published Year Pages File Type
394372 Information Sciences 2013 19 Pages PDF
Abstract

Active learning approach has been integrated with support vector machine or other machine-learning techniques in many areas. However, the challenge is: Unlabeled instances are often abundant or easy to obtain, but their labels are expensive and time-consuming to get in general. In spite of this, most existing methods cannot guarantee the usefulness of each query in learning a new classifier. In this paper, we propose a new active learning approach of selecting the most informative query for annotation. Unlabeled instance, which is nearest to the support vector machine’s hyperplane learnt from both the unlabeled instance itself and all labeled instances, is selected as the query for annotation. Merits of these queries in learning a new optimal hyperplane have been assured before they are annotated and put into the training set. Experimental results on several UCI datasets have shown the efficiency of our approach.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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