TWO-CLASS CLASSIFICATION WITH VARIOUS CHARACTERISTICS BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS AND SUPPORT VECTOR MACHINES

被引：1
|
作者：
Timotius, Ivanna Kristianti [1 ]
Setyawan, Iwan [1 ]
Febrianto, Andreas Ardian [1 ]
机构：
[1] Satya Wacana Christian Univ, Dept Elect Engn, Salatiga 50711, Indonesia
来源：
MAKARA JOURNAL OF TECHNOLOGY | 2011年 / 15卷 / 01期
关键词：
characteristic; classification; face recognition; kernel principal component analysis; support vector machines;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Two class pattern classification problems appeared in many applications. In some applications, the characteristic of the members in a class is dissimilar. This paper proposed a classification system for this problem. The proposed system was developed based on the combination of kernel principal component analysis (KPCA) and support vector machines (SVMs). This system has been implemented in a two class face recognition problem. The average of the classification rate in this face image classification is 82.5%.
引用
收藏
页码：96 / 100
页数：5
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