Self-organizing maps
Comparing Self-Organizing Maps
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Boosting unsupervised competitive learning ensembles
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Quality of adaptation of fusion ViSOM
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
A nonlinear projection method based on Kohonen's topology preserving maps
IEEE Transactions on Neural Networks
Ensemble Methods for Boosting Visualization Models
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
MACSDE: Multi-Agent Contingency Response System for Dynamic Environments
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
A weighted voting summarization of SOM ensembles
Data Mining and Knowledge Discovery
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Weighted Voting Superposition (WeVoS) is a novel summarization algorithm that may be applied to the results of an ensemble of topology preserving maps in order to identify the lowest topographical error in a map and thereby, to calculate the best possible visualization of the internal structure of its datasets. It is applied in this research to the food industry field that is studying the olfactory properties of Spanish dry-cured ham. The datasets used for the analysis are taken from the readings of an electronic nose, a device that can be used to recognize the sensory smellprints of Spanish dry-cured ham samples. They are then automatically analyzed using the previously mentioned techniques, in order to detect those batches with an anomalous smell (acidity, rancidity and different type of taints).. The Weighted Voting Superposition of ensembles of Self-Organising Maps (SOMs) is used here for visualization purposes, and is compared with the simple version of the SOM. The results clearly demonstrate how the WeVoS-SOM outperforms the simple SOM method.