Accurate alteative splice site (ASS) recognition is an important and difficult topic in the gene identification,and the average recognition rate is still ＜ 85％ [1].Many statistical patte recognition methods,such as neural networks (NNs)and support vector machine (SVM),were used for this task [2,3].Among them,SVM can construct a good highdimensional leaing model in the case of limited training set size and has good generalization ability,which exhibits many unique advantages in solving the small sample,non-linear,and high-dimensional patte recognition problems [4].To reduce the impact of noise samples on constructing optimal hyperplane,fuzzy SVM (FSVM) method was proposed [5].Each sample was assigned a different membership and had different contributions to the objective function.Because the noise samples had smaller memberships,and their effects on the separating hyperplane were reduced or eliminated.