Self-organizing maps
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IEEE Transactions on Neural Networks
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Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Learning Highly Structured Manifolds: Harnessing the Power of SOMs
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Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
On the impact of the metrics choice in SOM learning: some empirical results from financial data
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
On the profitability of scalping strategies based on neural networks
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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Results of neural network learning are always subject to some variability, due to the sensitivity to initial conditions, to convergence to local minima, and, sometimes more dramatically, to sampling variability. This paper presents a set of tools designed to assess the reliability of the results of self-organizing maps (SOM), i.e. to test on a statistical basis the confidence we can have on the result of a specific SOM. The tools concern the quantization error in a SOM, and the neighborhood relations (both at the level of a specific pair of observations and globally on the map). As a by-product, these measures also allow to assess the adequacy of the number of units chosen in a map. The tools may also be used to measure objectively how the SOM are less sensitive to non-linear optimization problems (local minima, convergence, etc.) than other neural network models.