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Design and implementation of a large-scale multi-class text classifier

Design and implementation of a large-scale multi-class text classifier
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摘要 Although, researchers in the ATC field have done a wide range of work based on SVM, almost all existing approaches utilize an empirical model of selection algorithms. Their attempts to model automatic selection in practical, large-scale, text classification systems have been limited. In this paper, we propose a new model selection algorithm that utilizes the DDAG learning architecture. This architecture derives a new large-scale text classifier with very good performance. Experimental results show that the proposed algorithm has good efficiency and the necessary generalization capability while handling large-scale multi-class text classification tasks. Although, researchers in the ATC field have done a wide range of work based on SVM, almost all existing approaches utilize an empirical model of selection algorithms. Their attempts to model automatic selection in practical, large-scale, text classification systems have been limited. In this paper, we propose a new model selection algorithm that utilizes the DDAG learning architecture. This architecture derives a new large-scale text classifier with very good performance. Experimental results show that the proposed algorithm has good efficiency and the necessary generalization capability while handling large-scale multi-class text classification tasks.
出处 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2005年第6期690-695,共6页 哈尔滨工业大学学报(英文版)
基金 SponsoredbytheScienceandTechnologyCommitteeofShanghaiMunicipalityKeyProject(GrantNo.02DJ14045)andtheMajorInternationalCoopera-tionProgramofNSFC(GrantNo.60221120145).
关键词 model selection DAGSVM automatic text classification 多级文本分类器 文字信息处理 自动文本分类 模型选择 支持向量机
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参考文献3

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  • 3HSU Chih-wei,LIN Chih-jen.A comparison of methods for multicalsss support vector machines[].IEEE Trans-actions on Neural Networks.2002

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