基于Tri-training的半监督SVM

当前机器学习面临的主要问题之一是如何有效地处理海量数据,而标记训练数据是十分有限且不易获得的。提出了一种新的半监督SVM算法,该算法在对SVM训练中,只要求少量的标记数据,并能利用大量的未标记数据对分类器反复的修正。在实验中发现,Tri-training的应用确实能够提高SVM算法的分类精度,并且通过增大分类器间的差异性能够获得更好的分类效果,所以Tri-training对分类器的要求十分宽松,通过SVM的不同核函数来体现分类器之间的差异性,进一步改善了协同训练的性能。理论分析与实验表明,该算法具有较好的学习效果。

Semi-supervised SVM based on Tri-training

One of the main difficulties in machine learning is how to solve large-scale problem effectively,and the labeled data are limited and fairly expensive to obtain.In this paper a new semi-supervised SVM is proposed.It applies Tri-training to improve SVM.The semi-supervised SVM uses a few labeled data to train few initial SVM classifiers and makes use of the large number unlabeled data to modify the classifier iteratively.Experiments on UCI dataset show that Tri-training can improve the classification accuracy...