摘要
随着云计算、大数据、互联网等多媒体技术的快速普及和发展,文本分类已经在多领域取得显著应用成效,因此文本分类已经成为百度、天猫、京东等各大搜索引擎准确运行的关键技术.该文详细地描述了BP神经网络算法及其应用现状,同时针对这些BP神经网络算法引入自适应共振理论,构建一个自适应的BP神经网络算法,与BP神经网络算法、K均值算法相比,实验结果表明文中的算法可以提高文本分类的准确度.
With the rapid development and popularization of cloud computing,big data,Internet and multi-media technology has achieved remarkable results in many application fields and accumulated themassive text data.So text classification has become the key technology of Baidu,Tmall,Jingdongand other major search engines and accurate operation.This paper describes the BP neural net-work algorithm and its application status.At the same time,according to the BP neural network al-gorithm using adaptive resonance theory,BP neural network algorithm were used to construct anadaptive,compared with the BP neural network K algorithm,K-means algorithm.The experimen-tal results show that this algorithm can improve the accuracy of text classification.
出处
《通化师范学院学报》
2018年第2期70-73,共4页
Journal of Tonghua Normal University
