Self-organization as an iterative kernel smoothing process
Neural Computation
Self-Organizing Maps
Visualizing asymmetric proximities with SOM and MDS models
Neurocomputing
Support vector machine classifiers for asymmetric proximities
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
k-Means clustering of asymmetric data
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Self organizing maps for visualization of categories
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Asymmetric clustering using the alpha-beta divergence
Pattern Recognition
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The paper presents an extension of the justification for use of the asymmetric Self-Organizing Map (SOM). We claim that it can successfully applied in the wider area of research than the textual data analysis. The results of our experimental study in the fields of sound recognition and heart rhythm recognition confirm this claim, and report the superiority of the asymmetric approach over the symmetric one, in both parts of our experiments.