On the study of GRBF and polynomial kernel based support vector machine in web logs

World Wide Web continues to grow day-by-day and it is difficult to track and understand users' need for the owners of a website. Therefore, an intelligent analyzer is required to find out the browsing patterns of a user. Moreover, the pattern which is revealed from this surge of web access logs must be useful, motivating, and logical. In this paper, two different kernel functions of support vector machine (SVM) are used to classify the web pages based on access time and region. Additionally, kernel parameters are also varied to study the trends of the accuracy of classification. Experimental results reveal that Gaussian radial basis function (GRBF) kernel based S VM is performing better than the polynomial kernel based SVM.