Sat, 26 Jul 2008 04:21:39 BST CiteULike: Group: Adaptive-Web - with tag classification http://www.citeulike.org/group/2118/tag/classification CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/group/2118/article/1137196 <i>IEEE Transactions on Knowledge and Data Mining, Vol. 16, No. 1. (January 2004), pp. 70-81.</i><br /><br />Web page classification is one of the essential techniques for Web mining because classifying Web pages of an interesting class is often the first step of mining the Web. However, constructing a classifier for an interesting class requires laborious preprocessing 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 nonhomepages (negative examples). In particular, collecting negative training examples requires arduous work and caution to avoid bias. The paper presents a framework, called positive example based learning (PEBL), for Web page classification which eliminates the need for manually collecting negative training examples in preprocessing. The PEBL framework applies an algorithm, called mapping-convergence (M-C), to achieve high classification accuracy (with positive and unlabeled data) as high as that of a traditional SVM (with positive and negative data). M-C runs in two stages: the mapping stage and convergence stage. In the mapping stage, the algorithm uses a weak classifier that draws an initial approximation of "strong" negative data. Based on the initial approximation, the convergence stage iteratively runs an internal classifier (e.g., SVM) which maximizes margins to progressively improve the approximation of negative data. Thus, the class boundary eventually converges to the true boundary of the positive class in the feature space. We present the M-C algorithm with supporting theoretical and experimental justifications. Our experiments show that, given the same set of positive examples; the M-C algorithm outperforms one-class SVMs, and it is almost as accurate as the traditional SVMs. PEBL: Web Page Classification without Negative Examples Hwanjo Yu Jiawei Han Kevin Chen-Chuan IEEE Transactions on Knowledge and Data Mining, Vol. 16, No. 1. (January 2004), pp. 70-81. 2007-03-02T20:28:36-00:00 2004 IEEE Transactions on Knowledge and Data Mining 16 1 70 81 classification mining pebl web http://www.citeulike.org/group/2118/article/777419 <i>New Review in Hypermedia and Multimedia, Vol. 12, No. 1. (June 2006), pp. 11-27.</i> Automated subject classification of textual Web pages, based on a controlled vocabulary: Challenges and recommendations Golub Koraljka doi:10.1080/13614560600774313 New Review in Hypermedia and Multimedia, Vol. 12, No. 1. (June 2006), pp. 11-27. 2006-07-28T08:31:19-00:00 2006 New Review in Hypermedia and Multimedia 1361-4568 12 1 11 27 Taylor and Francis Ltd classification ontology http://www.citeulike.org/group/2118/article/691964 <i>(1999)</i><br /><br />. We present an intelligent agent designed to compile a daily news program for individual users. Based on feedback from the user, the system automatically adapts to the user's preferences and interests. In this paper we focus on the system's user modeling component. First, we motivate the use of a multi-strategy machine learning approach that allows for the induction of user models that consist of separate models for long-term and short-term interests. Second, we investigate the utility of... A Hybrid User Model for News Story Classification D Billsus M Pazzani (1999) 2006-06-10T20:56:51-00:00 1999 classification news recommender user-profile