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%.
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Timotius, Ivanna Kristianti; Setyawan, Iwan; and Febrianto, Andreas Ardian
"Two-Class Classification with Various Characteristics Based on Kernel Principal Component Analysis and Support Vector Machines,"
Makara Journal of Technology: Vol. 15:
1, Article 15.
Available at: https://scholarhub.ui.ac.id/mjt/vol15/iss1/15
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