摘要
为减少人工分类的不确定性和分类错误,将文本分类技术引入专利自动分类系统,采用径向基函数神经网络(RBFNN)算法完成专利文本的训练和分类,并进行相关测试分析。实验结果表明,采用RBFNN分类器在专利文本自动分类中具有较理想的性能,测试平均F1值在70%以上。
In order to reduce the poor consistency and the errors in manual patent classification, this article introduces text classification technology into patent auto -classification system. It uses the radial basis function neural network algo- rithm to realize the automatic classification of patent text, and analyses the test samples. The experiment results show that this new system has a better classification results, and the average F1 value is higher than 70%
出处
《现代图书情报技术》
CSSCI
北大核心
2011年第12期58-63,共6页
New Technology of Library and Information Service
关键词
专利自动分类
文本分类
径向基函数神经网络
Patent automatic classification Text categorization Radial basis function neural network
