Optimizing the kernel selection for support vector machines using performance measures
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
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Nonlinear factor analysis method was studied by Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and a comparison with the related method kernel principle component analysis (KPCA) was made. It is pointed that the best error rate in handwritten digit recognition by kernel factor analysis (KFA) with varimax (4.2%) is competitive with KPCA (4.4%). The results indicate that KFA with varimax could more accurately image handwritten digit recognition and could be an effective measure for studying pattern recognition.