No abstract

In this paper, we focus on the classification problem to semisupervised learning. Semi-supervised learning is a learning task from both labeled and unlabeled data examples. We propose a novel semisupervised learning algorithm using a self-training framework and support vector machine. Self-training is one of the wrapper-based semisupervised algorithms in which the base classifier assigns labels to unlabeled data at each iteration and the classifier re-train on a larger training set at the next training step. However, the performance of this algorithm strongly depends on the selected newly-labeled examples. In this paper, a novel self-training algorithm is proposed, which improves the learning performance using the idea of the Apollonius circle to find neighborhood examples. The proposed algorithm exploits a geometric structure to optimize the self-training process. The experimental results demonstrate that the proposed algorithm can effectively improve the performance of the constructed classification model.