A Novel Kernel PCA Support Vector Machine Algorithm with Feature Transition Function

摘要

Based on the kernel function, this paper proposes an integrated classification method, combining the support vector machine (SVM) with kernel principle component analysis (KPCA), and its algorithm realization steps are also presented. Simulation experiment results show that the current approach has excellent classification performance, which is suitable for the pattern recognition and eliminate the influence of noise.