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It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of large scale terms and data patterns. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, there has been often held the hypothesis...
On the basis of analyzing the basic concepts and the process of text excavation, the present study proposes some new methods in extraction of text features, deflation of characteristic collection, extraction of study and knowledge pattern, and appraisal of model quality. Meanwhile, it makes a comparison of two types of text categorization, text classifications and text cluster, and it briefly explores...
Video artificial text detection is a challenging problem of pattern recognition. Current methods which are usually based on edge, texture, connected domain, feature or learning are always limited by size, location, language of artificial text in video. To solve the problems mentioned above, this paper applied SOM (Self-Organizing Map) based on supervised learning to video artificial text detection...
Issues of synonymy and strong relational semantic information increase the feature dimension of text vector, which embarrasses the efficiency and precision of text classification. In order to decrease the feature dimension of text vector, a method of text feature extraction based on hybrid parallel genetic clustering algorithm was proposed in this paper. Firstly, K-means algorithm is used to perform...
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