The paper presents a learning method, called iterative crosstraining (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 (CoTraining). The experimental results show that ICT gives the best performance, followed by S-Bayes, CoTraining U-Bayes-EM and S-Word.
['Boonserm Kijsirikul', 'Puay Sasiphongpairoege', 'Nuanwan Soonthornphisaj', 'Surapant Meknavin']
Supervised and unsupervised learning algorithms for thai web pages identification