燕山大学学报2016，Vol.40Issue(5)：438-445,8.
基于改进的softmax回归模型的话题跟踪算法
A topic tracking algorithm based on modified softmax regression
摘要
Abstract
The purpose of topic tracking is to recognize the category of the given topics in the news data, it plays an important role in the development trend of the Internet news and public opinion analysis. In this paper, A Softmax linear model based on cosine assumption has been posed, which enhance the effect of topic tracking through analyzed several topics tracking algorithm that commonly used.And for the input features, a novel feature weighted algorithm, C CHI, is given after summarized several feature item weighted algorithm based on the vector space model, which use category difference factor to improve the chi square statistic based on vector space model. Experiments based on the standard corpus TDT4 show that the work of this paper can improve the performance of topic tracking effectively.关键词
类别信息/特征权重/softmax回归/话题跟踪Key words
category information/feature weighting/softmax regression/topic tracking分类
信息技术与安全科学引用本文复制引用
朴乘锴,袁方,刘宇,王煜..基于改进的softmax回归模型的话题跟踪算法[J].燕山大学学报,2016,40(5):438-445,8.基金项目
河北省科技计划项目 （）
