Support vector machine(SVM) is a novel type of learning machine

this thesis introduces the theory of SVM briefly and application in a classification system for texture image

and discusses in detail the core techniques and algorithms

which combine SVM and distance classification into two layer serial classifier. SVM has shown to provide better generalization performance than traditional techniques. However

because using Quadratic Programming (QP) optimization techniques

the training of SVM is time consuming

especially when the training data set is very large. So we have two classifiers combined. Firstly

a rejecting coefficient and rejecting rule are defined. According the rejecting rule

the distance classifier can classify the images and give the final results

or reject to classify the input images. The rejected images are fed into SVM for further classification. The algorithms can take advantages of SVM and distance classification. The experiments show that the algorithms have low error rate and high speed.