A Novel Inductive Semi-supervised SVM with Graph-Based Self-training
A Novel Inductive Semi-supervised SVM with Graph-Based Self-training
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DOI：
10.1007/978-3-642-36669-7_11
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发表时间：
2012-10
影响因子：
4.8
通讯作者：
Shengjun Cheng;Qingcheng Huang;Jiafeng Liu;Xianglong Tang
中科院分区：
文献类型：
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作者：
Shengjun Cheng;Qingcheng Huang;Jiafeng Liu;Xianglong Tang
本文提出了一种用于半监督学习的新型归纳支持向量机 IS3VM，旨在通过自训练引导未标记数据来改进支持向量机。 SVM 分类器通过训练集的扩充进行迭代细化。提出一种改进的自训练方法，采用邻域图来保证新增训练样例的可靠性。具体来说，在自训练过程的每次迭代中，局部切边权重统计用于帮助估计新标记的示例是否可靠，并且仅使用可靠的自标记示例来扩大标记的训练集。实验表明，改进的自训练是有益的，所提出的 IS3VM 算法可以有效地利用未标记的数据来获得更好的性能，并且可以与最先进的半监督 SVM 相媲美。
In this paper, a novel inductive support vector machine for semi-supervised learning, named IS3VM, is proposed, which aims to improve SVM by bootstrapping unlabeled data with self-training. The SVM classifier is iteratively refined through the augmentation of the training set. An improved self-training method is given by employing neighborhood graph for guarantying the reliability of newly added training examples. In detail, in each iteration of the self-training process, the localcut edge weightstatistic is used to help estimate whether a newly labeled example is reliable or not, and only the reliable self-labeled examples are used to enlarge the labeled training set. Experiments show that, the improved self-training is beneficial and the proposed IS3VM algorithm can effectively exploit unlabeled data to achieve better performance, and is comparable to the-state-of-the-art semi-supervised SVM.