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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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
Print ISBN: 978-3-540-46479-2
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