Application Of Combination And Optimization Algorithm Of SVM Kernel To Down Category Recognition System

Supprot Vector Machine(SVM) is a new general learning method developed in recent years on the goundation of Structural Risk Minimization principle and VC Dimension of Statistical Learning Theory and by searching the best trade-off base on the limited samples information between pattern complexity and learning ability. Currently, SVM algorithm is more precise than or equal to traditional algorithm in pattern recognition .Kernel function is one of the tunable parameters in SVM classifier. Since form and parameters of kernel function decide the type and the complexity of classifier,it should act as a means of controling the performance of classifer. In this paper, we address the selective problem of kernel function and its parameters in SVM modeling. Our work is proceeding in down category recognition and address improving the recognition rate of system,which run as follows:1.On the base of researching theories of SVM and the methods of image processing,we address the study of SVM kernel and its parameters, and SVM kernel optimization method is proposed to improve the performance of system. In down recognition,we decide the best kernel and its parameter values by the method and improve the recogniton rate of the triangle nodes of down with the SVM model, so the triangle nodes which have been recognized can be matched each other and the distance between the matched triangle nodes is calculated. in the end, the down category which is duck's down or goose down,or other one is recognized and integral recognization rate can be improved.2.We make a study of the problem about constructing a hybrid kernel, then combine kernel construction method and kernel optimization method and research together. After test and compare the different types of kernels, we select the optimal kernel and its parameters for down category recognition system, and the software of down category automatic recognition system is designed and perfected. In the end, we develop a system with friendly interface and simple operation with VC++ 6.0 in Windows2000.In the paper, the down category automatic recognition base on SVM conquers some disadvantages and inconveniences of manual identification, and decreases the faults of manual identification. Besides, the system can improve the level of down category recognition and our study has a wide range of further use and practicality. Even it fills up the blank in the domain at home and abroad.