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Tsallis Mutual Information for Document Classification
AbstractMutual information is one of the mostly used measures for evaluating image similarity. In this paper, we investigate the application of three different Tsallis-based generalizations of mutual information to analyze the similarity between scanned documents. These three generalizations derive from the Kullback–Leibler distance, the difference between entropy and conditional entropy, and the Jensen–Tsallis divergence, respectively. In addition, the ratio between these measures and the Tsallis joint entropy is analyzed. The performance of all these measures is studied for different entropic indexes in the context of document classification and registration.
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Vila, M.; Bardera, A.; Feixas, M.; Sbert, M. Tsallis Mutual Information for Document Classification. Entropy 2011, 13, 1694-1707.View more citation formats
Vila M, Bardera A, Feixas M, Sbert M. Tsallis Mutual Information for Document Classification. Entropy. 2011; 13(9):1694-1707.Chicago/Turabian Style
Vila, Màrius; Bardera, Anton; Feixas, Miquel; Sbert, Mateu. 2011. "Tsallis Mutual Information for Document Classification." Entropy 13, no. 9: 1694-1707.
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