Research for Face Recognition Base on Mized Kernel Function
Support Vector Machines were developed in recent years, which have large advantage over the traditional neural network on small sample set for classification. In all research fields of these learning machines, the selection of kernel function is the most important problem, which has a closed relationship with the performance of classification. But the research work in this field is not enough. In this paper we evaluate the performance of usual kernel functions for SVM theoretically, through observing and computing the kernel matrix. Base on this, we used the selected kernel functions to get a new mixed kernel function. Experiential data proved that the performance of SVM was improved by the mixed kernel function. If we select the weighted values properly, the correct rate even is 100%. This will not only gives us a method to get a new learning machine, but also give a reference for selecting kernel function.
Shuxian Zhu Renjie Zhang
Collage of Optical and Electronic Engineering, University of Shanghai for Science and Technology, Shanghai 200093, china
国际会议
2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)
镇江
英文
1395-1399
2008-07-07（万方平台首次上网日期，不代表论文的发表时间）