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- W94500917 abstract "Support Vector Machines and Kernel methods have become very popular as methods for learning from examples. In this paper, in order to solve the non-linear and the time of recognition problem, recent advanced techniques are presented. For example, Kernel Principal Components and Proximal Support Vector Machines, Kernel Principal Components Analysis can describe multiple correlations between pixels in the image recognition, and can handle the high order statistics of the original data, at the same time the nonlinear features of the image can be preferably extracted. Proximal Support Vector Machines is an improvement for these characters of Support Vector Machines, combines the proximal algorithm with Support Vector Machines, has a better ability of nonlinear mapping and a stronger generalization capability, and the pace of Proximal Support Vector Machines is very enhanced. In this paper, the computer simulation was progressed based on ORL face database, the effectiveness of the Proximal Support Vector Machines algorithm and Kernel Principal Components Analysis algorithm were shown by experimental results." @default.
- W94500917 created "2016-06-24" @default.
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- W94500917 date "2012-07-27" @default.
- W94500917 modified "2023-09-23" @default.
- W94500917 title "Face Recognition Technology Based on Kernel Principal Components and Proximal Support Vector Machines" @default.
- W94500917 doi "https://doi.org/10.1109/mace.2012.287" @default.
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