Abstract
The paper presents a learning method, called iterative cross-training (ICT) for identifying Thai Web pages. Our method combines two classifiers, i.e. a word segmentation classifier and a naive Bayes classifier, that use unlabeled examples to train each other. We compare ICT against other supervised and unsupervised learning methods: a supervised word segmentation classifier (S-Word), a supervised naive Bayes classifier (S-Bayes), an unsupervised naive Bayes classifier using the EM algorithm (U-Bayes-EM), and a co-training-style classifier (Co Training). The experimental results show that ICT gives the best performance, followed by S-Bayes, CoTraining U-Bayes-EM and S-Word.
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Kijsirikul, B., Sasiphongpairoege, P., Soonthornphisaj, N., Meknavin, S. (2000). Supervised and Unsupervised Learning Algorithms for Thai Web Pages Identification. In: Mizoguchi, R., Slaney, J. (eds) PRICAI 2000 Topics in Artificial Intelligence. PRICAI 2000. Lecture Notes in Computer Science(), vol 1886. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44533-1_69
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DOI: https://doi.org/10.1007/3-540-44533-1_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67925-7
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