APPLICATION OF SUPPORT VECTOR MACHINES IN MEDICAL DATA

被引：0
|
作者：
Weng, Yongqiang [1 ]
Wu, Chunshan [2 ]
Jiang, Qiaowei [2 ]
Guo, Wenming [2 ]
Wang, Cong [2 ]
机构：
[1] Beijing Univ Posts & Telecommun, Sch Automat, Trusted Distributed Comp & Serv Lab, Beijing 100876, Peoples R China
[2] Beijing Univ Posts & Telecommun, Sch Software Engn, Trusted Distributed Comp & Serv Lab, Beijing 100876, Peoples R China
来源：
PROCEEDINGS OF 2016 4TH IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND INTELLIGENCE SYSTEMS (IEEE CCIS 2016) | 2016年
关键词：
support vector machine; incremental learning; fuzzy c-mean; generalized KKT conditions;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Compared with ordinary data, medical data has its own characteristics. Such as mode of polymorphism, incomplete and longer timeliness. These characteristics brought a lot of difficulties on medical data of collection and processing, so the incremental learning method in the application of medical data is particularly critical. In this paper, Based on the support vector machines (SVM) proposed an incremental learning method that combined with fuzzy c-average and generalized KKT conditions. Through the filter of historical sample set and new sample that is invalid to reduce the training sample. So as to achieve rapid, incremental learning. Finally, the improved algorithm applied to the two standard medical database from UCI, which verify the improved algorithm advantage.
引用
收藏
页码：200 / 204
页数：5
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