To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space (TCS2FCM) is introduced for classifying texts.
为了改善文本聚类的准确度，提出用基于主题概念子空间的模糊c -均值聚类(TCS2FCM)方法来分类文本。
A tag weight model based on topic generalization is advanced, by which the tag weight is calculated automatically. Experiment results show that this model performs well in XML retrieval.
提出了用主题概括强度衡量XML标签权重的方法，实验结果显示，该方法能有效提高XML检索的质量。
The aim of this paper is studying the categorizing and retrieving method for multi-format information resources based on Topic Maps.
本文研究基于主题图的多种格式信息资源的分类管理与检索方法。
2012-2015, Semantic Image Understanding and Recognition Based on Topic Discovery, Natural Science Foundation of China (NSFC), Co-PI.
基于主题发现的图像语义理解与识别，国家自然科学基金(地区科学基金项目)，2012- 2015，参与。
2012-2015, Semantic Image Understanding and Recognition Based on Topic Discovery, Natural Science Foundation of China (NSFC), Co-PI.
基于主题发现的图像语义理解与识别，国家自然科学基金(地区科学基金项目)，2012- 2015，参与。
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