| 局部线性与One-Class结合的科技文本分类方法 |
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| 引用本文： | 姚力群,陶卿.局部线性与One-Class结合的科技文本分类方法[J].计算机研究与发展,2005,42(11):1862-1869. |
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| 作者姓名： | 姚力群 陶卿 |
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| 作者单位： | 中国科学院自动化研究所,北京,100080;中国人民解放军炮兵学院二系,合肥,230031 |
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| 基金项目： | 国家“九七三”重点基础研究发展规划基金项目（G1998030500）;国家自然科学基金项目（60175023） |
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| 摘 要： | 结合了局部线性和One-Class的思想对科技文本分类问题进行了研究，利用局部线性的思想寻找文本样本的内在支撑流形，利用One-Class的思想确定正负样本的分界面．与K近邻算法、线性SVM算法和One-Class问题的SVM算法相比，给出的科技文本分类方法具有分类精度高、参数估计简便、正负样本分类精度可控制等优点，为解决科技文献的分类问题提供了一条有效的途径．
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| 关 键 词： | 局部线性 科技文献 One-Class 文本分类 |
| 收稿时间： | 2004-05-14 |
| 修稿时间： | 2004-05-142004-08-30 |
Journal Text Categorization with the Combination of Local Linearity and One-Class |
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| Yao Liqun,Tao Qing.Journal Text Categorization with the Combination of Local Linearity and One-Class[J].Journal of Computer Research and Development,2005,42(11):1862-1869. |
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| Authors: | Yao Liqun Tao Qing |
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| Affiliation: | 1. Institute of Automation, Chinese Academy of Sciences, Beijing 100080; 2.2rd Department, New Star Research Institute of Applied Technology, Hefei 230031 |
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| Abstract: | A research is proposed on journal text categorization with the combination of local linearity and one-class. Local linearity is introduced to determine the samples' low-dimensional manifold, which could be regarded as the distribution of the samples in low-dimensional mapping spaces. At the same time, the border of positive and negative samples is determined by one-class. Compared with Knearest algorithm, linear SVM and one-class SVM, the new algorithm of journal text categorization gives better results in high precision, simple parameter estimation and easy control of risks, which gives an effective approach for the solution of text categorization. |
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| Keywords: | local linearity technical literature one-class text categorization |
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