摘要
目前众多文本分类方法已经得到了广泛的应用,然而针对不同的语言结构,各分类方法的泛化能力也有差异,因此本文利用机器学习算法中的GaussianNB模型对藏文新闻类文本语料进行分类,检验该分类模型在藏文语言结构中具有良好的分类性能.分类过程中首先以一码元为文本特征,采用特征频度统计方法,形成特征值向量,然后对特征向量进行降维处理,最后通过分类实验结果,验证了该模型对藏文文本具有良好的分类效果.
In this paper,an SIR model with a saturated treatment function is studied.The satura-tion treatment function is a description of the effect of being delayed for treatment in the case of limited medical resources and a large number of patients.The result of the system undergoing Bogdanov-Takens bifurcation with the change of parameters is proved.Finally,the correctness of the conclusion is demonstrated intuitively by numerical simulation.
作者
苏慧婧
群诺
贾宏云
HUANG Chun-xian;ZHOU Xiao-liang(School of Mathematics and Statistics,Minnan Normal University,Zhangzhou 363000,China;School of Mathematics and Statistics,Lingnan Normal University,Zhanjiang 524048,China)
出处
《青海师范大学学报（自然科学版）》
2019年第4期1-4,54,共5页
Journal of Qinghai Normal University(Natural Science Edition)
基金
西藏自治区教育厅“计算机及藏文信息技术国家级团队和重点实验室建设”(藏教财指[2018]81号)
关键词
藏文文本分类
文本特征
GaussianNB模型
SIR model
saturated treatment function
Bogdanov-Takens bifurcation
homoclinic loop
numerical simulation
