支持向量机的进化多核设计-控制理论与应用.pdf

28 6 Vol. 28 No. 6 2011 6 Control Theory Applications Jun. 2011 :2011 , , , , (1. 502, 710025; 2. , 621000; 3. 204, 262500) : , . , , ; , . . , . : ; ; ; : TP181 : A Evolutionary multiple kernels design for support vector machines LI Ren-bing , LI Ai-hua , BAI Xiang-feng , CAI Yan-ping , WANG De-sheng (1. No. 502 Faculty, the Second Artillery Engineering College, Xi’an Shaanxi 710025, China; 2. China Aerodynamics Research and Development Center, Mianyang Sichuan 621000, China; 3. No. 204 Faculty, the Second Artillery Petty Officer School, Qingzhou Shandong 262500, China) Abstract: To boost the classification accuracy of support-vector-machines(SVM), we propose an algorithm with evo- lutionary multiple kernels(EMK), based on the genetic programming(GP). In this algorithm, each individual represents a multiple kernel function, and is encoded by the tree-structure for enhancing the non-linearity of the multiple kernel function. Grow method is applied to initialize the GP population, from which the EMK adapting to practical problems is obtained by genetic operations. No priori knowledge is required due to the global search of GP. Comparisons of experimental re- sults of EMK with the single kernel function and other multiple kernel functions show that EMK effectively improves the classification performance of SVM. Key words: evolutionary multiple kernels; genetic programming; support vector machines; kernel function 1 (Introduction) .