| 基于贝叶斯模型的专利分类 |
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| 引用本文： | 郭炜强,文军,文贵华.基于贝叶斯模型的专利分类[J].计算机工程与设计,2005,26(8):1986-1987,1996. |
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| 作者姓名： | 郭炜强 文军 文贵华 |
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| 作者单位： | 华南理工大学,计算机研究所,广东,广州,510641 |
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| 基金项目： | 国家自然科学基金项目（60003019）. |
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| 摘 要： | 朴素贝叶斯分类器理论基础好，分类精度高。利用特征词权重函数修改朴素贝叶斯分类器，进而利用它实现专利文本的自动分类，不仅减少了专利人工分类的工作量和分类错误，而且为技术跟踪、竞争分析等提供了有效支持。实验与应用表明改进的朴素贝叶斯分类器用来解决专利分类是有效的。
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| 关 键 词： | 专利 朴素贝叶斯分类器 专利分类 特征词权重 文本挖掘 |
| 文章编号： | 1000-7024（2005）08-1986-02 |
| 收稿时间： | 2004-07-16 |
| 修稿时间： | 2004-07-16 |
Patent categorization based on Bayes model |
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| GUO Wei-qiang,WEN Jun,WEN Gui-hua.Patent categorization based on Bayes model[J].Computer Engineering and Design,2005,26(8):1986-1987,1996. |
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| Authors: | GUO Wei-qiang WEN Jun WEN Gui-hua |
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| Abstract: | Based on naive bayes classifier having solid theory foundation and high accuracy rate of classification, the classical naive bayes classifier was firstly improved by using term weight function in text, and then the patent categorization was implemented. This approach not only reduced manual labor and the categorization error, but also supported for the technology tracing, competition intelligence etc.The experiments and applications illustrate that the improved naive bayes classifier can be utilized to classify patents efficiently. |
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| Keywords: | patent naive bayes classifier patent classification term weight function text mining |
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