A research on Web resources automatic classification using SVMs
Cai Wei, Wang Yongcheng, Yin Zhong-hang, Zou Tao · 2003
With the rapid growth of Web information, text categorization has become an important research field for the management of Internet information. Most of the existing methods are based on traditional statistics, which provides a conclusion only for the situation where sample size is tending to infinity. So they may not work well in practical case with limited samples and easily lead to the problem of over-fitting. These papers theoretical analyze the reason of over-fitting and introduce its condition as well as the method to solve it. We introduce SVMs, a method to avoid over-fitting, which is based on statistical learning theory matching the limited number of Internet news examples.