| 基于元样本稀疏表示分类器的文本资源分类 |
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| 引用本文： | 范少萍,郑春厚,王召兵.基于元样本稀疏表示分类器的文本资源分类[J].图书情报工作,2011,55(16):115-118. |
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| 作者姓名： | 范少萍 郑春厚 王召兵 |
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| 作者单位： | 1. 曲阜师范大学信息技术与传播学院;2. 曲阜师范大学日照校区图书馆; |
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| 摘 要： | 首先分析文本分类的现状，根据文本分类算法的要求和稀疏表示分类算法（SRC）的思想，设计基于元样本的稀疏表示分类器（MSRC），并应用于文本分类研究。实验结果表明，该MSRC算法具有较好的文本分类效果，有助于提高基于内容的信息检索效率。
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| 关 键 词： | 文本分类 稀疏表示分类 元样本 MSRC |
| 收稿时间： | 2011-04-18 |
Metasample Based Sparse Representation Classification for Text Classifying |
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| Fan Shaoping Zheng Chunhou Wang Zhaobing College of Information Technology , Communication,Qufu Normal University,Rizhao Library of Rizhao Campus,Rizhao.Metasample Based Sparse Representation Classification for Text Classifying[J].Library and Information Service,2011,55(16):115-118. |
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| Authors: | Fan Shaoping Zheng Chunhou Wang Zhaobing College of Information Technology Communication Qufu Normal University Rizhao Library of Rizhao Campus Rizhao |
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| Institution: | 1. College of Information Technology and Communication, Qufu Normal University,;2. Library of Rizhao Campus, Qufu Normal University,; |
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| Abstract: | Text classification is an important step in text preprocessing.Efficient text categorization can help to improve the efficiency of content-based information retrieval.This paper firstly analyzes the status of text classification study.Then,based on the requirements of text classification,the authors propose a metasample based sparse representation classification algorithm according to the theory of SRC algorithm.The experimental results on the text classification prove that the proposed algorithm is efficie... |
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| Keywords: | text classification sparse representation classification metasample MSRC |
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