Dynamic Distance-Based Active Learning with SVM

Authors:
Jun Jiang;Horace H. Ip
Affiliations:
Image Computing Group, Department of Computer Science,;Image Computing Group, Department of Computer Science, and Center for Innovative Applications of Internet and Multimedia Technologies, (AIMtech Centre), City University of Hong Kong, Hong Kong
Venue:
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2007

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Abstract

In this paper, we present a novel active learning strategy, named dynamic active learning with SVM to improve the effectiveness of learning sample selection in active learning. The algorithm is divided into two steps. The first step is similar to the standard distance-based active learning with SVM [1] in which the sample nearest to the decision boundary is chosen to induce a hyperplane that can halve the current version space. In order to improve upon the learning efficiency and convergent rates, we propose in the second step, a dynamic sample selection strategy that operates within the neighborhood of the "standard" sample. Theoretical analysis is given to show that our algorithm will converge faster than the standard distance-based technique and using less number of samples while maintaining the same classification precision rate. We also demonstrate the feasibility of the dynamic selection strategy approach through conducting experiments on several benchmark datasets.