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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Hsu, A., & Halgamuge, S. K. (2006). Semi-supervised learning of dynamic self-organising maps. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4232 LNCS, pp. 915–924). Springer Verlag. https://doi.org/10.1007/11893028_102
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