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. © 2012 Springer Science+Business Media New York.
A new training method for large self organizing maps
Rizzo;Riccardo
2013
Abstract
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. © 2012 Springer Science+Business Media New York.File in questo prodotto:
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