Support Vector Machine In The Marshalling Yard Recognition

Support Vector Machines (SVM) is a kind of novel machine learning methods. It can solve small-sample learning problems better by using Experiential Risk Minimization in place of Structural Risk Minimination. Moreover, this theory can change the problem in non-linearity space to that in the linearity space in order to reduce the algorithm complexity by using the kernel function idea. SVM have become the hotspot of machine learning because of their excellent learning performance. They also have successful applications in many fields, such as: face detection, handwriting digit recognition, text auto-categorization, etc. But as a new technique, SVM also have many shortcomings that need to be researched, including: the adaptive kernel and parameter selection, sensitive to noise, have a limitation in the scale of training set, the shortcomings of training methods, incremental learning, and the combination with the prior knowledge, etc. The applications in many fields are limited because of these problems. In this paper, some of above problems are probed into the application in image processing area. This sisertation mainly focuses on some applications of SVM in image processing area after studing the theory of SVM.The work including:(1) The base theory of optimization algorithmThe essence of SVM trainingmethod is to solve Quadratic Programming(QP). In this paper, SMO and SOR has algorithm been introduced in brief and the essence of improved training method is alsodiscussed.(2) Learn and introduce kernel theory and kernel function.(3) Discriminating rail-station by knowledge above and SVM-training.Draw some good examination resulte and conclusion by the examination of rail-station discriminating. In the end ,the rail-station can be outlined by the method of SVM in practice.and theapproach scheme of damage analysis also be brought forward.