The Self-Organizing Maps: Background, Theories, Extensions and Applications

@inproceedings{Yin2008TheSM,
  title={The Self-Organizing Maps: Background, Theories, Extensions and Applications},
  author={Hujun Yin},
  booktitle={Computational Intelligence: A Compendium},
  year={2008}
}
  • Hujun Yin
  • Published in
    Computational Intelligence: A…
    2008
  • Computer Science
For many years, artificial neural networks (ANNs) have been studied and used to model information processing systems based on or inspired by biological neural structures. They not only can provide solutions with improved performance when compared with traditional problem-solving methods, but also give a deeper understanding of human cognitive abilities. Among various existing neural network architectures and learning algorithms, Kohonen’s selforganizing map (SOM) [46] is one of the most popular… 

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TLDR
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TLDR
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TLDR
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References

SHOWING 1-10 OF 145 REFERENCES

Self-Organising Maps for Pattern Recognition

Adaptive, associative, and self-organizing functions in neural computing.

TLDR
This paper contains an attempt to describe certain adaptive and cooperative functions encountered in neural networks, to reason what functions are readily amenable to analytical modeling and which phenomena seem to ensue from the more complex interactions that take place in the brain.

The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data

TLDR
The motivation was to provide a model that adapts its architecture during its unsupervised training process according to the particular requirements of the input data, and by providing a global orientation of the independently growing maps in the individual layers of the hierarchy, navigation across branches is facilitated.

Self-Organizing Neural Networks for Visualisation and Classification

TLDR
It has been demonstrated, that the usage of an artificial neural network, Kohonen’s self organizing feature map, for visualisation and classification of high dimensional data, can be used also for knowledge acquisition and exploratory data analysis purposes.

Generalizing self-organizing map for categorical data

TLDR
A generalized self-organizing map model is proposed that offers an intuitive method of specifying the similarity between categorical values via distance hierarchies and, hence, enables the direct process of categoricalvalues during training.

Representation Of Sensory Information In Self-Organizing Feature Maps, And Relation Of These Maps To Distributed Memory Networks

TLDR
This paper contains some new results which show that both of the above functions, viz. formation of the internal representations and their storage, can be implemented simultaneously by an adaptive, massively parallel, self-organizing network.

Recursive self-organizing maps

Self-organizing maps: ordering, convergence properties and energy functions

TLDR
It is proved that the learning dynamics cannot be described by a gradient descent on a single energy function, but may be described using a set of potential functions, one for each neuron, which are independently minimized following a stochastic gradient descent.

Self-organization of orientation sensitive cells in the striate cortex

A nerve net model for the visual cortex of higher vertebrates is presented. A simple learning procedure is shown to be sufficient for the organization of some essential functional properties of
...