Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neural maps and topographic vector quantization
Neural Networks
Self-Organizing Maps
Neural Computation and Self-Organizing Maps; An Introduction
Neural Computation and Self-Organizing Maps; An Introduction
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
A nonlinear projection method based on Kohonen's topology preserving maps
IEEE Transactions on Neural Networks
Dynamics and Topographic Organization of Recursive Self-Organizing Maps
Neural Computation
Alternative learning vector quantization
Pattern Recognition
Decreasing Neighborhood Revisited in Self-Organizing Maps
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Self-organizing maps with refractory period
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Adaptive FIR neural model for centroid learning in self-organizing maps
IEEE Transactions on Neural Networks
A parameter in the learning rule of SOM that incorporates activation frequency
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Self-Organizing Map Formation with a Selectively Refractory Neighborhood
Neural Processing Letters
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Self-organizing maps (SOMs) are widely used in several fields of application, from neurobiology to multivariate data analysis. In that context, this paper presents variants of the classic SOM algorithm. With respect to the traditional SOM, the modifications regard the core of the algorithm, (the learning rule), but do not alter the two main tasks it performs, i.e. vector quantization combined with topology preservation. After an intuitive justification based on geometrical considerations, three new rules are defined in addition to the original one. They develop interesting properties such as recursive neighborhood adaptation and non-radial neighborhood adaptation. In order to assess the relative performances and speeds of convergence, the four rules are used to train several maps and the results are compared according to several error measures (quantization error and topology preservation criterions).