Hyperparameter selection for self-organizing maps
Neural Computation
Self-organizing maps
GTM: the generative topographic mapping
Neural Computation
Bayesian approach for neural networks—review and case studies
Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Kohonen Maps
GTM: A Principled Alternative to the Self-Organizing Map
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Joint entropy maximization in kernel-based topographic maps
Neural Computation
Kernel-based topographic map formation achieved with an information-theoretic approach
Neural Networks - New developments in self-organizing maps
Class distribution on SOM surfaces for feature extraction and object retrieval
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Model-based clustering by probabilistic self-organizing maps
IEEE Transactions on Neural Networks
Computer Science - Research and Development
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The Self-Organizing Map, SOM, is a widely used tool in exploratory data analysis. A theoretical and practical challenge in the SOM has been the difficulty to treat the method as a statistical model fitting procedure. In this chapter we give a short review of statistical approaches for the SOM. then we present hte probability density model for which the SOM training gives the maximum likelihood estimate. The density model can be used to choose the neighborhood width of the SOM so as to avoid overfitting and to improve the reliability of the results. The density model also gives tools for systematic analysis of teh SOM. A major application of teh SOM is the analysis of dependencies between variables. We discuss some difficulties in the visual analysis of the SOM and demonstrate how quantitative analysis of the dependencies between variables. We discuss some difficulties in the visual analysis of the SOM and demonstrate how quantitative analysis of the dependencies can be carried out by calculating conditional distributions from the density model.