Topology-conserving maps for learning visuo-motor-coordination
Neural Networks
Self-Organizing Maps
A supervised training algorithm for self-organizing maps for structures
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Analytic Comparison of Self-Organising Maps
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Interactive object-based retrieval using relevance feedback
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Improving performance of self-organising maps with distance metric learning method
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Self-Organising maps for classification with metropolis-hastings algorithm for supervision
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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The supervised self-organizing map consists in associating output vectors to input vectors through a map, after self-organizing it on the basis of both input and desired output given altogether. This paper compares the use of Euclidian distance and Mahalanobis distance for this model. The distance comparison is made on a data classification application with either global approach or partitioning approach. The Mahalanobis distance in conjunction with the partitioning approach leads to interesting classification results.