Support Vector Machine with Fuzzy Kernel Function Based on Similarity Measure

Based on the statistical learning theory,kernels are often presented as measures of similarity measure.Kernels play a very important role in support vector machine (SVM) algorithms,which can mapper the un-classification data to high dimensional space,to get the optimal classification result.Unfortunately,SVM lacks ability to deal with the system or data with ambiguous characters.So this paper proposes to SVM with fuzzy kernel,which is obtained by similarity measure method.Compared to Gaussian RBF kernel function and fuzzy sigmoid kernel function,SVM with fuzzy similarity kernel gets more accuracy results with less computation requirements,and alleviates current limitation of the fuzzy kernel function.