Evolutionary support center machine

Support vector machines (SVMs) are powerful tools in machine learning community, but it is not easy to select suitable parameters for them. And, very often SVMs show slow speeds in test phase due to their large number of support vectors. To remedy SVMs deficiencies, we propose a novel SVM-like method, which is called evolutionary support center machine (ESCM) in this paper. The key idea behind ESCM is to apply evolutionary algorithm to construct the separation hyperplane with the similar form to those constructed by SVMs in an incremental way. ESCM can not only optimize the support centers and tune the kernel parameters adaptively, but also control the number of support centers appropriately. Numerical experiments on several UCI benchmarks verify the efficiency of ESCM.