Growing Neural Gas for Vision Tasks with Time Restrictions

  • José García
  • Francisco Flórez-Revuelta
  • Juan Manuel García
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


Self-organizing neural networks try to preserve the topology of an input space by means of their competitive learning. This capacity is being used for the representation of objects and their motion. In addition, these applications usually have real-time constraints. In this work, diverse variants of a self-organizing network, the Growing Neural Gas, that allow an acceleration of the learning process are considered. However, this increase of speed causes that, in some cases, topology preservation is lost and, therefore, the quality of the representation. So, we have made a study to quantify topology preservation using different measures to establish the most suitable learning parameters, depending on the size of the network and on the available time for its adaptation.


Topographic Function Input Space Delaunay Triangulation Gesture Recognition Vision Task 
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

  • José García
    • 1
  • Francisco Flórez-Revuelta
    • 1
  • Juan Manuel García
    • 1
  1. 1.Department of Computer Tecnology and ComputationUniversity of Alicante. Apdo. 99AlicanteSpain

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