报告详情
Research on Testing Text Classification based on the Sequential Minimal Optimization by Support Vector Machine Method
编号：135
访问权限：仅限参会人
更新：2024-10-23 10:02:34 浏览：133次
张贴报告
报告开始：暂无开始时间（Asia/Shanghai）
报告时间：暂无持续时间
所在会场：[暂无会议] [暂无会议段]
暂无文件
摘要
There are a lot of testing or measuring data would be generated during test or measuring, how to classify the data to further analysis is a very important research appoint. Text classification technology is one of the research priorities in the field of information science, and support vector machine (SVM) has an obvious advantage in solving the problem on text classification. In this paper, according to analyzing the characteristics of English text, we study the whole process of English text classification, build a specific method by improved SVM, and design a program which is suitable for English text classification. Improved sequential minimal optimization (SMO) algorithm is used as the basis of binary classification, and the multi-class classification is realized by using the one-to-one classification method. Simulation results show that our classification model is suitable for different kinds of English texts, and it consistently achieves good performance on text classification tasks. Evaluation of the various indicators on classification can reach bigger than 90%.
关键词
Support vector machine (SVM); text classification; Sequential minimal optimization (SMO)；Multi-class Classification
报告人
WangBing
Dr Harbin University稿件作者
全部评论
重要日期
-
会议日期
10月31日
2024
至11月03日
2024
-
09月30日 2024
初稿截稿日期
-
11月12日 2024
注册截止日期
主办单位
Anhui University
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
联系方式
- Mrs. icsmd
- ic******@163.com
发表评论