Research On The Method Of Support Vector Machine Classification

The rapid development of computers technologies and communication technologies needs for effective classification methods to classify information resources, but traditional classification methods are laborious and time-consuming which make automatic text classification more popular. Classification of samples is a classification model based on criteria of risk minimization. Support vector machine is very good to deal with the issue of text categorization, so the text classification method based on support vector machines becomes a research focus in this area. Although support vector machine algorithm have been verified in many applications, there are still some areas for improvement.In this paper, the basic theory of classification and support vector machine are studied, and then clustering method is applied to the support vector machine for reducing the number of samples and improving the training speed of support vector machine. At the same time, Haar wavelet transform is applied to the support vector machine for decreasing dimensionality of training samples and test samples for support vector machine, which can improve the training and test speed. At the end of this article, the application effect of cluster analysis and wavelet transform is summarized, which is based on the analysis of experimental data. Cluster analysis and wavelet transform are used to process samples of support vector machines. From the purpose, both of them are aimed to improve training time and classification efficiency, and the difference is that they use different strategies. Cluster analysis uses the strategy of reducing the number of samples while wavelet transform uses the strategy of depressing dimensionality of samples. From the result, the effect of wavelet transform is better than the cluster analysis. they both reduced, to some extent, the training and classification time, but the wavelet transform can better guarantee the accuracy of classification.