Abstract
For SVMs, large training samples will lead to high computing complexity of convex quadratic programming, even difficulty in running. Sample selection as a preprocessor of classification can greatly reduce the computational cost of training and test. In this paper, we present an enhanced sample selection frame based on convex structure for SVM. By learning the approximate errors of chosen set, we realize automatic control of sample scale for SVMs. Experimental results on face recognition show that our sample selection methods can adaptively select fewer high quality samples while maintaining the classification accuracy of SVM.
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Zhou, X., Jiang, W., Tan, J. (2014). Enhanced Sample Selection for SVM on Face Recognition. In: Han, W., Huang, Z., Hu, C., Zhang, H., Guo, L. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8710. Springer, Cham. https://doi.org/10.1007/978-3-319-11119-3_36
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DOI: https://doi.org/10.1007/978-3-319-11119-3_36
Publisher Name: Springer, Cham
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