【摘要】 <正> 1 引言随着Internet快速普及和发展,使得网络上的电子文档数量激增。用户在享受它所提供的大量信息的同时,也越来越感到被庞大复杂的信息所淹没。然而网络上的文档数据并不是被有组织地管理,而仅仅是一个大的无序数据集合。在网络上寻找自己需要的信息常常要花费大量的时间和精力。如果对这些文档数据进行良好的索引和归纳,将有助于把文档数更多还原

【Abstract】 In the field of text categorization,the number of unlabeled documents is generally much gretaer than that of labeled documents. Text categorization is the problem of categorization in high-dimension vector space, and more training samples will generally improve the accuracy of text classifier. How to add the unlabeled documents of training set so as to expand training set is a valuable problem. The theory of active learning is introducted and applied to the field of text categorization in this paper,exploring the method of using unlabeled documents to improve the accuracy of text classifier. It is expected that such technology will improve text classifier’s accuracy through adopting relatively large number of unlabelled documents samples. We brought forward an active learning based algorithm for text categorization,and the experiments on Reuters news corpus showed that when enough training samples available,it’s effective for the algorithm to promote text classifier’s accuracy through adopting unlabelled document samples.更多还原