Semi-supervised Learning of Dynamic Self-Organising Maps

  • Arthur Hsu
  • Saman K. Halgamuge
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


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.


Class Label Supervise Learning Class Structure Benchmark Dataset Data Label 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Arthur Hsu
    • 1
  • Saman K. Halgamuge
    • 1
  1. 1.Dynamic Systems and Control Group, Department of Mechanical and Manufacturing EngineeringUniversity of MelbourneAustralia

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