TWO-CLASS CLASSIFICATION WITH VARIOUS CHARACTERISTICS BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS AND SUPPORT VECTOR MACHINES
Ivanna K. Timotius, Iwan Setyawan, Andreas Ardian Febrianto · MAKARA of Technology Series · 2011
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%.