Unsupervised Learning with Dynamic Cell Structures (DCS) Neural Network

Version 1.1.0.0 (12,2 ko) par Ilias Konsoulas
Learns data clusters and their topology in n-dimensional space using the DCS-GCS neural network.
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Mise à jour 20 sept. 2013

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The Dynamic Cell Structure (DCS-GCS) ANN belongs to the class of Topology Representing Networks (TRN's). It can learn supervised and unsupervised. Here, the unsupervised learning mode is implemented and demonstrated. It's learning method employs a combination of modified Kohonen learning to adjust the neuron's positions, with a sort of competitive Hebbian learning for its connections. For details please consult ref. [1]. In order to make the main script (dcs.m) functional, you must first select and generate a manifold (data) using the corresponding data generator.

REFERENCE
[1] Bruske J., Sommer G., "Dynamic Cell Structure Learns Perfectly Topology Preserving Map", Neural Computation, vol. 7, Issue 4, July 1995, pp. 845-865.

Citation pour cette source

Ilias Konsoulas (2024). Unsupervised Learning with Dynamic Cell Structures (DCS) Neural Network (https://www.mathworks.com/matlabcentral/fileexchange/43572-unsupervised-learning-with-dynamic-cell-structures-dcs-neural-network), MATLAB Central File Exchange. Récupéré le .

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Data Generators/

Final/

Version Publié le Notes de version
1.1.0.0

Minor changes on Data Generators files.
1.0.0.0