document: PEBL : positive example based learning for Web page classification using SVM Web page classification is one of the essential techniques for Web mining . Specifically , classifying Web pages of a user-interesting class is the first step of mining interesting information from the Web . However , constructing a classifier for an interesting class requires laborious pre-processing such as collecting positive and negative training examples . For instance , in order to construct a `` homepage '' classifier , one needs to collect a sample of homepages ( positive examples ) and a sample of non-homepages ( negative examples ) . In particular , collecting negative training examples requires arduous work and special caution to avoid biasing them . We introduce in this paper the Positive Example Based Learning ( PEBL ) framework for Web page classification which eliminates the need for manually collecting negative training examples in pre-processing . We present an algorithm called Mapping-Convergence ( M-C ) that achieves classification accuracy ( with positive and unlabeled data ) as high as that of traditional SVM ( with positive and negative data ) . Our experiments show that when the M-C algorithm uses the same amount of positive examples as that of traditional SVM , the M-C algorithm performs as well as traditional SVM .
doc_bio_tags: O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O