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- W13661906 abstract "Two important research areas in statistical approaches for automated text categorization are similarity-based learning algorithms and associated thresholding strategies. The combination of these techniques significantly influences the overall performance of text categorization systems. After researching common techniques in both areas, we describe a lazy linear classifier known as the keyword association network (KAN) and a rank-in-score (RinSCut) thresholding strategy to improve the categorization performance over existing techniques. Extensive experiments have been conducted on the Reuters-21578 data set. The experimental results show that KAN outperforms two linear classifiers, Rocchio and Widrow-Hoff, and gives results competitive with k-NN. All implemented classifiers except Widrow-Hoff show performance improvements with RinSCut." @default.
- W13661906 created "2016-06-24" @default.
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- W13661906 date "2002-01-01" @default.
- W13661906 modified "2023-09-28" @default.
- W13661906 title "KAN and RinSCut: Lazy Linear Classifier and Rank-in-Score Threshold in Similarity-Based Text Categorization" @default.
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