Learning Algorithm Based on Fuzzy Support Vector Machine of Multi-Core Functions

In order to solve those question which a kernel function can not meet, such as heterogeneous or irregular data, large sample size, uneven distribution of the sample the actual application requirements, and obtain better results, the author of this paper proposes a support vector machine algorithm based on multi-core fuzzy. Fuzzy kernel weights of this decision tree algorithm is mainly determined by the fuzzy factors of the sample. The simulation data show that multi-core fuzzy support vector machine has the good the superiority compared with the traditional single-kernel function support vector machine.