Abstract
Semi-supervised classification uses a large amount of unlabeled data to help a little amount of labeled data for designing classifiers, which has good potential and performance when the labeled data are difficult to obtain. This paper mainly discusses semi-supervised classification based on CPN (Counter-propagation Network). CPN and its revised models have merits such as simple structure, fast training and high accuracy. Especially, its training scheme combines supervised learning and unsupervised learning, which makes it very conformable to be extended to semi-supervised classification problem. According to the characteristics of CPN, we propose a semi-supervised dynamic CPN, and compare it with other two semi-supervised CPN models using Expectation Maximization and Co-Training/Self-Training techniques respectively. The experimental results show the effectiveness of CPN based semi-supervised classification methods.
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© 2006 Springer-Verlag Berlin Heidelberg
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Chen, Y., Qian, Y. (2006). Semi-supervised Dynamic Counter Propagation Network. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_59
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DOI: https://doi.org/10.1007/11811305_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
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