Web Text Classification Based On Svm

With the rapid development of the Internet, the Web information becomes richer and richer. The increasing sharply data leads to the fact that a lot of important information is hidden. With the existent searching engine, the users may roughly find what they want on the Internet. However, the way in which the resources are not exactly fit for the users need, and one more important reason is that it can not quickly and effectively abstract the useful information from the many Web data, which bring us the conflict between vast information and little knowledge. Data mining should be applied to the text information in order to extract the useful pattern that is interested and potential and the hidden information from the substantive, heterogeneous and unstructured data sources. This is web text mining. With the rapidly development of the web text data, web text mining have been an important study direction in data mining area.Many techniques have been applied in text categorization, such as the Nearest Neighbore method, Bayesian Networks ,decision trees, neural networks, support vector machines, vector space model, regression model, etc. In this paper, we introduce web text categorization methods based on improved SVM.(1)This text introduces the basic concept, kind and method of web data raining, with the concrete procedure and correlated theories of web text mining.(2)We study the Statistical Learning Theory (SLT) and Support Vector Machine (SVM) Theory seriously. We explain the research and application status of Support Vector Machine and point out some important issues which is to be resolved when researchers do further research of SVM.(3) We introduce an improved parallel SVM methods used to web text categorization.(4)We introduce an improved active learning SVM methods used to web text categorization.