Back propagation is sensitive to initial conditions
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Neural network design
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble of structure-adaptive self-organizing maps for high performance classification
Information Sciences: an International Journal - methods and systems for intelligent human—computer interaction
A Dynamic Adaptive Self-Organising Hybrid Model for Text Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Applied Intelligence
Topology constraint free fuzzy gated neural networks for pattern recognition
IEEE Transactions on Neural Networks
A granular computing framework for self-organizing maps
Neurocomputing
Learning Optimal Parameters in Decision-Theoretic Rough Sets
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Game-theoretic risk analysis in decision-theoretic rough sets
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Incorporating game theory in feature selection for text categorization
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Analysis of data-driven parameters in game-theoretic rough sets
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Fundamenta Informaticae - Advances in Rough Set Theory
Analyzing uncertainties of probabilistic rough set regions with game-theoretic rough sets
International Journal of Approximate Reasoning
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Self-Organizing Maps (SOM) is a powerful tool for clustering and discovering patterns in data. Competitive learning in the SOM training process focuses on finding a neuron that is most similar to that of an input vector. Since an update of a neuron only benefits part of the feature map, it can be thought of as a local optimization problem. The ability to move away from a local optimization model into a global optimization model requires the use of game theory techniques to analyze overall quality of the SOM. A new algorithm GTSOM is introduced to take into account cluster quality measurements and dynamically modify learning rates to ensure improved quality through successive iterations.