Abstract : In this paper, we focus on the classification problem to semi-supervised learning. Semi-supervised learning is a learning task from both labeled and unlabeled data examples. We propose a novel semi-supervised learning algorithm using a self-training framework and support vector machine. Self-training is one of the wrapper-based semi-supervised 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.
https://hal.inria.fr/hal-03165381 Contributor : Hal IfipConnect in order to contact the contributor Submitted on : Wednesday, March 10, 2021 - 4:05:11 PM Last modification on : Wednesday, March 10, 2021 - 4:12:51 PM Long-term archiving on: : Friday, June 11, 2021 - 7:06:37 PM
File
Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed
until : 2023-01-01