Self-organizing maps
Speeding up the Self-Organizing Feature Map Using Dynamic Subset Selection
Neural Processing Letters
A New SOM Initialization Algorithm for Nonvectorial Data
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I
Improved SOM learning using simulated annealing
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Improved SOM Algorithm-HDSOM Applied in Text Clustering
MINES '10 Proceedings of the 2010 International Conference on Multimedia Information Networking and Security
A comparison between habituation and conscience mechanism in self-organizing maps
IEEE Transactions on Neural Networks
Quantifying the neighborhood preservation of self-organizing feature maps
IEEE Transactions on Neural Networks
A Quick Assessment of Topology Preservation for SOM Structures
IEEE Transactions on Neural Networks
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Self Organizing Maps (SOMs) are widely used neural networks for classification or visualization of large datasets. Like many neural network simulations, implementations of the SOM algorithm need a scan of all the neural units in order to simulate the work of a parallel machine. This paper reports a new learning algorithm that speeds up the training of a SOM with a little loss of the performance on many quality tests. The very low computation time, means that this algorithm can be used as a fast visualization tool for large multidimensional datasets.