A unifying objective function for topographic mappings
Neural Computation
GTM: the generative topographic mapping
Neural Computation
A stochastic self-organizing map for proximity data
Neural Computation
A Topological Hierarchical Clustering: Application to Ocean Color Classification
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Lateral Interactions in Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Self-Organizing Graphs - A Neural Network Perspective of Graph Layout
GD '98 Proceedings of the 6th International Symposium on Graph Drawing
A Self-Organising Approach to Multiple Classifier Fusion
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Data visualisation and manifold mapping using the ViSOM
Neural Networks - New developments in self-organizing maps
Self-organizing maps and clustering methods for matrix data
Neural Networks - 2004 Special issue: New developments in self-organizing systems
A self-organizing map of sigma-pi units
Neurocomputing
Nonlinear Principal Manifolds --- Adaptive Hybrid Learning Approaches
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Model-based clustering by probabilistic self-organizing maps
IEEE Transactions on Neural Networks
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
The infomin criterion: an information theoretic unifying objective function for topographic mappings
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Soft topographic map for clustering and classification of bacteria
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Adaptive nonlinear manifolds and their applications to pattern recognition
Information Sciences: an International Journal
Multiple classifier fusion performance in networked stochastic vector quantisers
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Weighted topological clustering for categorical data
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Soft topographic maps for clustering and classifying bacteria using housekeeping genes
Advances in Artificial Neural Systems
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In this paper Bayesian methods are used to analyze some of theproperties of a special type of Markov chain. The forwardtransitions through the chain are followed by inverse transitions(using Bayes' theorem) backward through a copy of the same chain;this will be called a folded Markov chain. If an appropriatelydefined Euclidean error (between the original input and its"reconstruction" via Bayes' theorem) is minimized with respect tothe choice of Markov chain transition probabilities, then thefamiliar theories of both vector quantizers and self-organizingmaps emerge. This approach is also used to derive the theory ofself-supervision, in which the higher layers of a multilayernetwork supervise the lower layers, even though overall there is noexternal teacher.