Topology representing networks
Neural Networks
Self-Organizing Maps
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Hand Gesture Recognition Following the Dynamics of a Topology-Preserving Network
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Hybrid GNG Architecture Learns Features in Images
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
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Self-organizing neural networks try to preserve the topology of an input space by means of their competitive learning. This capacity has been used, among others, for the representation of objects and their motion. In addition, these applications usually have real-time constraints. In this work we have study a kind of self-organizing network, the Growing Neural Gas with different parameters, to represent different objects. In some cases, topology preservation is lost and, therefore, the quality of the representation. So, we have made a study to quantify topology preservation to establish the most suitable learning parameters, depending on the kind of objects to represent and the size of the network.