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This paper works on the most intensively studied algorithm- k Nearest Neighbor algorithm. The purpose is to investigate the performance of different similarity measures in the kNN on Chinese texts. The two measures that we focus on are cosine value and Jensen-Shannon Divergence. We use both the corpus collected from the Sogou, whose data extracts from the website of Sohu.com, and datasets that we...
This paper studies the principle of text categorization in which Jensen-Shannon Divergence is used to calculate text similarity, comparing its accuracy of classification and time taking to the traditional Cosine Similarity algorithm. Experimental research shows that Jensen-Shannon Divergence algorithm will reach better results when test materials remain unchanged.
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