摘要
广义特征值最接近支持向量机(GEPSVM)是近年提出的一种两分类方法.本文结合GEPSVM的平面特点和流形学习,给出一类半监督学习算法SemiGEPSVM.该方法不仅仍保持对诸如XOR问题的分类能力,而且在每类仅有一个有标样本的极端情形下,仍具有适用性.当已标样本不能用于构建超平面时,本文采用k-近邻方法选择样本并标记类别.一旦已标样本的个数可构建超平面时,采用本文的选择方法标记样本.此外,本文还从理论上证明该算法存在全局最优解.最后,SemiGEPSVM算法的有效性在人工数据集和标准数据集上得到验证.
A binary classifier, proximal support vector machine via generalized eigenvalues （GEPSVM）, has been proposed recently. In this paper, with the characteristics of plane classifiers and manifold learning, an effective semi-supervised algorithm SemiGEPSVM is proposed. It keeps the performance of handling XOR problems and is suitable for more challenges, even with only one labeled sample per class. While the number of labeled samples is not satisfactory to generate plane, k-nearest neighbor is used to select the unlabelled samples. Otherwise, the proposed sample selection method with plane characteristics is adopted. Furthermore, it is proved that the proposed selection method is global optimization. And the experimental results of SemiGEPSVM are verified on one toy problem and some benchmark datasets.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2009年第3期349-353,共5页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金资助项目(No.60603029
60773061)
关键词
支持向量机
半监督学习
流形学习
Support Vector Machine, Semi-Supervised Learning, Manifold Learning
