Abstract The large-scale labeled samples can not be acquired easily and the cost of sample labeling is high. Aiming at these problems, an active learning algorithm of support vector machine (SVM) based on tri-training semi-supervised learning and convex-hull vector is proposed in this paper. Semi-supervised learning and active learning are efficiently combined. Firstly, by calculating the convex-hull vector of the sample set, samples of convex-hull vector which are most likely to be support vectors are selected to be labeled. For the existing active learning, the unlabeled samples are no longer used after the most informative samples are selected to be labeled. Secondly, to salve this problem, semi-supervised learning method-based tri-training is introduced into SVM active learning. Thus, the unlabeled samples with higher confidence level of classifying samples are selected and classified as the training sample set, and the useful information for learning machines in the unlabeled samples is exploited. The experimental results on UCI dataset show that the proposed algorithm achieves higher classification accuracy with less labeled samples and it improves generalization performance and reduces the labeling cost of SVM training.
About author:: (Xu Hailong (Corresponding author)， born in 1981， Ph.D.， Lecturer. His research interests include pattern recognition， support vector machines， target assignment， etc.)(LONG Guangzheng， born in 1975， Ph.D.， Associate Professor. His research interests include target recognition and assignment， combat simulation， etc.)(BIE Xiaofeng， born in 1979， Ph.D.， Associate Professor. His research interests include target assignment， combat evaluation and simulation， etc.)(WU Tian′ai， born in 1977， Ph.D candidate， Lecturer. Her research interests include target tracking and recognition.)(GUO Pengsong， born in 1979， Master， Associate Professor. His research interests include target assignment， combat evaluation and simulation.)
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2016, Vol. 29 
