Two-Class Classification with Various Characteristics Based on Kernel Principal Component Analysis and Support Vector Machines
- Resource Type
- Authors
- Iwan Setyawan; Andreas Ardian Febrianto; Ivanna K. Timotius
- Source
- Makara Journal of Technology, Vol 15, Iss 1, Pp 96-100 (2011)
- Subject
- characteristic, classification, face recognition, kernel principal component analysis, support vector machines
Contextual image classification
business.industry
Computer science
Linear classifier
Pattern recognition
Machine learning
computer.software_genre
Kernel principal component analysis
Support vector machine
Relevance vector machine
Kernel method
ComputingMethodologies_PATTERNRECOGNITION
lcsh:TA1-2040
Radial basis function kernel
Least squares support vector machine
Artificial intelligence
business
lcsh:Engineering (General). Civil engineering (General)
computer
- Language
- English
- ISSN
- 2356-4539
2355-2786
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%.