Text Message Authorship Classification Using Kernel Support Vector Machines

被引：0
|
作者：
Kretchmar, Matt [1 ]
Zhao, Yifu [1 ]
机构：
[1] Denison Univ, Dept Math & Comp Sci, Granville, OH 43023 USA
来源：
2014 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI), VOL 2 | 2014年
关键词：
Machine Learning Applications; Classification; SVM; NLP; Text Messages;
D O I：
10.1109/CSCI.2014.121
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We explore the application of Kernel Support Vector Machines (SVM) to the realm of text messages. Our intent is to classify the author of a text message based on usage patterns present in a training set of text messages. We achieve between 57% and 96% accuracy in determining the author of unknown samples.
引用
收藏
页码：215 / 218
页数：4
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