Iterative cross-training: An algorithm for learning from unlabeled Web pages

The paper presents a learning method, called Iterative Cross-Training (ICT) , for classifying Web pages in two classification problems, i.e., (1) classification of Thai/non-Thai Web pages, and (2) classification of course/non-course home pages. Given domain knowledge or a small set of labeled data, our method combines two classifiers that are able to effectively use unlabeled examples to iteratively train each other. We compare ICT against the other learning methods: supervised word segmentation classifier, supervised naïve Bayes classifier, and co-training-style classifier. The experimental results, on two classification problems, show that ICT gives better performance than those of the other classifiers. One of the advantages of ICT is that it needs only a small set of pre-labeled data or no pre-labeled data in the case that domain knowledge is available.