Mixture Models for Web Page Classification

摘要：

This paper presents a method for designing semisupervised classifier trained on labeled and unlabeled instances. We explore the trade-off maximizing a generative likelihood of labeled and unlabeled data. Moreover, mixture models are an interesting and flexible model family. The different uses of mixture models include for example generative models and density estimation. This paper investigates semi-supervised learning of mixture models using a unified objective function taking both labeled and unlabeled data into account. We conducted experiments on the WebKB and 20NEWSGROUPS. The results show that unlabeled data results in improvement in classification accuracy over the supervised model.

关键词： semi-supervised learning mixture model generative model

作者: Bai JingHua Zhang XiaoXian Li ZhiXin Li XiaoPing

作者单位: School of Computer Science and technology Beijing Institute of Technology Beijing, P.R. China Department of software Changchun Institute of Technology ChangChun, P.R. China

会议类型: 国际会议

会议名称: 2010 Second Asia-Pacific Conference on Information Processing(2010年第二届亚太地区信息处理国际会议 APCIP 2010)

会议地点: 南昌

会议语种:英文

页码: 80-83

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）