Abstract
Automatic categorization has been shown to be an accurate alternative to manual categorization in which documents are processed and automatically assigned to pre-defined categories. The accuracy of different methods for categorization has been studied largely, but their efficiency has seldom been mentioned. Aiming to maintain effectiveness while improving efficiency, we proposed a fast algorithm for text categorization and a compressed document vector representation method based on a novel class space model. The experiments proved our methods have better efficiency and tolerable effectiveness.
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Gao, Y., Ma, R., Liu, Y. (2006). Fast Text Categorization Based on a Novel Class Space Model. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_96
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DOI: https://doi.org/10.1007/11925231_96
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