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GECCO
2009
Springer

The impact of network topology on self-organizing maps

10 years 9 months ago
The impact of network topology on self-organizing maps
In this paper, we study instances of complex neural networks, i.e. neural networks with complex topologies. We use Self-Organizing Map neural networks whose neighborhood relationships are defined by a complex network, to classify handwritten digits. We show that topology has a small impact on performance and robustness to neuron failures, at least at long learning times. Performance may however be increased (by almost 10%) by evolutionary optimization of the network topology. In our experimental conditions, the evolved networks are more random than their parents, but display a more heterogeneous degree distribution. Categories and Subject Descriptors I.2.6 [Computing Methodologies]: ARTIFICIAL INTEL
Fei Jiang, Hugues Berry, Marc Schoenauer
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where GECCO
Authors Fei Jiang, Hugues Berry, Marc Schoenauer
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