Fuzzy multiple kernel support vector machine(SVM) combines fuzzy SVM with multiple kernel learning(MKL) method which effectively reduces the sensitivity to noises and learning difficulty with the multi-source and heterogeneous data of the traditional SVM model by utilizing membership functions and combinations of multiple kernel functions. Fuzzy multiple kernel SVM has been widely applied in the pattern recognition and artificial intelligence community. This paper summarized the theoretical basis of fuzzy multiple kernel SVM and its current research status. Specifically, this paper were comprehensively reviewed the key problems of the fuzzy multiple kernel SVM, i. e., the design of fuzzy membership functions and MKL methods. Finally, this paper prospected the future research of fuzzy multiple kernel SVM.

[2021-10-05] Printed Article /doc/2021.01.0035/v2