A novel support vector regression method—FRSVR(fuzzy robust support vector regression) based on traditional ε-SVR is proposed in this paper. First

a solution for support vector regression with arbitrary cost function is given. Second the properties which a cost function should have in order to construct a robust support vector regression are discussed. Then a family of cost functions is introduced. In the training procedure of FRSVR

outliers can also be detected in terms of different training error ranges between normal examples and outliers using fuzzy c-means algorithm (FCM). Through iteration

FRSVR is obtained. Since it is based on ε-SVR

various optimization methods for epsilon support vector regression can be used to solve FRSVR. In the experimental part of the paper

FRSVR is applied to process emulsified oil combustion flame images such that outliers therein can be detected and removed

then flame shapes are accordingly segmented. Experimental results show that FRSVR performs better than ε-SVR and ASBF filter in the sense of removing outliers and enhancing the generalization ability.