Abstract：In this paper, the property is used that any matrices can be decomposed into symmetric positive semi-definite ones using extremum decomposition. It is used on RBF kernel to generate a new kernel, called Ked. Combining Ked with global poly kernel, a mixed kernel with good classification performance is constructed. The classification experiments on UCI database are deployed with the mixed kernel. Compared with RBF kernel, the experimental results show that mixed kernel can decrease the number of support vectors and has better classification performance. Furthermore, it has good training time when the value of RBF kernel parameter is small.
业巧林，业宁，张训华. 基于极分解下的混合核函数及改进*[J]. 模式识别与人工智能, 2009, 22(3): 366-373.
YE Qiao-Lin, YE Ning, ZHANG Xun-Hua. Extremum Decomposition Based Mixtures of Kernels and Its Improvement. , 2009, 22(3): 366-373.
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