主题主要有聚类，稀疏编码，局限玻尔兹曼机和深度信念网络。
Topics include clustering, sparse coding, autoencoders, restricted Boltzmann machines, and deep belief networks.
针对词、潜在概念、文本和主题之间的模糊关系，提出一种基于信息论的潜在概念获取与文本聚类方法。
To emphasize the fuzzy relation among words, latent concepts, text and topics, an information theory based approach to latent concept extraction and text clustering is proposed.
最后，使用增量式的概念格生成算法对搜索结果片段进行概念聚类，并从中产生每个聚类的主题。
Finally, incremental algorithm of producing concept lattice is used to carry on concept clustering to the passage of search results, and produced the theme of each cluster result from it.
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。
To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space (TCS2FCM) is introduced for classifying texts.
检索结果聚类技术致力于实时地将检索结果按主题整理成若干类别，并赋予每个类别一个准确可读的标签。
The search result clustering technique is dedicated to group search results into topics on the fly and gives each group an accurate and readable label.
本文详细阐述了进行主题驱动搜索的索引结构、主题聚类方法、主题覆盖网络的构造与维护算法。
This paper presented a topic driven search mechanism to improve the search capacity of the existing structured P2P system.
文本聚类，即将给定的文本集合划分为多个簇，从而达到簇内文本的主题相关性，簇间文本的主题无关性的目的。
Document clustering is to separate the document set into groups, in each group the documents are of the same or related topic.
文本聚类，即将给定的文本集合划分为多个簇，从而达到簇内文本的主题相关性，簇间文本的主题无关性的目的。
Document clustering is to separate the document set into groups, in each group the documents are of the same or related topic.
应用推荐