Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
