On the Emulation of Kohonen's Self-Organization via Single-Map Metropolis-Hastings Algorithms
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Improved SOM learning using simulated annealing
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Learning activity patterns using fuzzy self-organizing neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling the manifolds of images of handwritten digits
IEEE Transactions on Neural Networks
Improving performance of self-organising maps with distance metric learning method
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Fuzzy supervised self-organizing map for semi-supervised vector quantization
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
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Self-Organising Maps (SOM) provide a method of feature mapping from multi-dimensional space to a usually two-dimensional grid of neurons in an unsupervised way. This way of data analysis has been proved as an efficient tool in many applications. SOM presented by T.Kohonen originally were unsupervised learning algorithm, however it is often used in classification problems. This paper introduces novel method for supervised learning of the SOM. It is based on neuron's class membership and Metropolis-Hastings algorithm, which control network's learning process. This approach is illustrated by performing recognition tasks on nine real data sets, such as: faces, written digits or spoken letters. Experimental results show improvements over the state-of-art methods for using SOM as classifier.