Choice For Kernel Function Of Support Vector Machine (svm) Method Is Discussed In This Paper

Support Vector Machine is a new machine learning method based on Statistical Learning Theory. The method inherited the many excellent concept of statistical learning theory, such as VC dimension, the theory of minimum structure risk. The theory as a foundation, puts forward the optimal hyperplanes concept, used to generate decision function. Kernels are used in SVM to map the learning data (non-linearly) into a higher dimensional feature space where the computational power of the linear learning machine is increased, It can get rid of the "dimension disaster" problem. A kind of good method SVM used in the small sample, non-linearly sample and High dimensional pattern recognition. The Logical Choice of kernel function can increase the mapping of linear degree. So, the Choice of kernel function and related parameters is important to improve the Sample separability.Every kernel function has its advantages and disadvantages, its to the effect of mapping non-linearity samples is different, the method of selection kernel function in the study of SVM is very-important. The contents of my paper is introduced to the method of selection kernel function based on the distribution characteristics of samples. First of all, the paper analyze the distribution characteristics of samples base on Mathematics description method, secondly, According to different distribution characteristics to select the kernel function; and then, the introduction of the combination of kernel function selection metho base on it, experiments show that the SVM classifier generalization ability have increased significantly; Finally, the paper introduces a kind method of selection kernel function combinations base on rank diversity of kernel matrices. These research results, do the simulation experiment.Through these content of research, increase the method of selection kernel function, SVM learning ability and generalization ability was improved.