Semi-supervised Learning of Dynamic Self-Organising Maps (original) (raw)
Abstract
We present a semi-supervised learning method for the Growing Self-Organising Maps (GSOM) that allows fast visualisation of data class structure on the 2D network. Instead of discarding data with missing values, the network can be trained from data with up to 60% of their class labels and 25% of attribute values missing, while able to make class prediction with over 90% accuracy for the benchmark datasets used.
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Authors and Affiliations
- Dynamic Systems and Control Group, Department of Mechanical and Manufacturing Engineering, University of Melbourne, Victoria, 3010, Australia
Arthur Hsu & Saman K. Halgamuge
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- Arthur Hsu
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Editors and Affiliations
- Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong
Irwin King - Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
Jun Wang - The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
Lai-Wan Chan - Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus
DeLiang Wang
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© 2006 Springer-Verlag Berlin Heidelberg
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Hsu, A., Halgamuge, S.K. (2006). Semi-supervised Learning of Dynamic Self-Organising Maps. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028\_102
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- DOI: https://doi.org/10.1007/11893028\_102
- Publisher Name: Springer, Berlin, Heidelberg
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