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Labeled datasets are essential for text categorization. They are used to train a classifier, or as a benchmark collection to evaluate categorization algorithms. However, labeling a large-scale document set is extremely expensive because it involves much human labour, and the labeling process itself is subjective rather than objective. Therefore, labels assigned to documents by only one human editor...
Text clustering is the key technology for topic detection, and topic detection is essentially similar to the unsupervised clustering. However, general clustering is based on global information, and clustering in the topic detection is based on incremental ways. So we should study topic detection according to clustering algorithm, and it is necessary for clustering algorithm to be in-depth and extensive...
Feature selection is an important preprocessing step of Chinese Text Categorization, which reduces the high dimension and keeps the reduced results comprehensible compared to feature extraction. A novel criterion to filter features coarsely is proposed, which integrating the superiorities of term frequency-inverse document frequency as inner-class measure and CHI-square as inter-class, and a new feature...
Text classification is the key technology for topic tracking, and vector space model (VSM) is one of the most simple and effective model for topics representation. Feature selection algorithm in VSM is an important means of data pre-processing, and it can reduce vector space dimension and improve the generalization ability of the algorithm. Therefore, it is necessary for feature selection algorithms...
A blind watermarking algorithm for the embedding of MIDI (Musical Instrument Digital Interface) information into the binary music score image based on the OMR (Optical Music Recognition) technology is presented in this paper, and the information of digital music score and MIDI can be reconstructed. The experiment results show that the algorithm has no effect to the precision of music score recognition,...
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