Machine Learning Algorithms in Web Page Classification

Abstract

In this paper we use machine learning algorithms like SVM, KNN and GIS to perform a behavior comparison on the web pages classifications problem, from the experiment we see in the SVM with small number of negative documents to build the centroids has the smallest storage requirement and the least on line test computation cost. But almost all GIS with different number of nearest neighbors have an even higher storage requirement and on line test computation cost than KNN. This suggests that some future work should be done to try to reduce the storage requirement and on list test cost of GIS.