Self-Organizing Maps

@inproceedings{Kohonen1995SelfOrganizingM,
  title={Self-Organizing Maps},
  author={Teuvo Kohonen},
  booktitle={Springer Series in Information Sciences},
  year={1995}
}
  • T. Kohonen
  • Published in
    Springer Series in…
    1995
  • Computer Science
The Self-Organising Map (SOM) algorithm was introduced by the author in 1981. Its theory and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it. The most important practical applications are in exploratory data analysis, pattern recognition, speech analysis, robotics, industrial and medical diagnostics, instrumentation, and control, and literally hundreds of other tasks. In this monograph… Expand

Topics from this paper

Generalization of the Self-Organizing Map: From Artificial Neural Networks to Artificial Cortexes
TLDR
This paper presents a generalized framework of a self-organizing map (SOM) applicable to more extended data classes rather than vector data, in which each reference vector unit of a conventional SOM is replaced by a functional module. Expand
Application of Self-Organizing Maps to the Maritime Environment
  • V. Lobo
  • Geography, Computer Science
  • IF&GIS
  • 2009
TLDR
An overview of different applications of SOMs in maritime problems is presented and a simple and intuitive explanation of how a SOM is trained is provided, together with a formal explanation of the algorithm. Expand
Limited scope learning for self-organizing map and its applications
TLDR
This paper propos "Limited Scope Learning" on SOM, which is able to get the feature map that is shown by distributed expression, i.e., more than one winner is selected out of the whole map at the learning time. Expand
Lazy Self-Organizing Map and its behaviors
TLDR
This study proposes the Lazy Self-Organizing Map (LSOM) algorithm which reflects the world of worker ants and applies LSOM to various input data set and confirms that LSOM can obtain a more effective map reflecting the distribution state of the input data than the conventional SOM. Expand
Disconnecting Self-Organizing Map
The Self-Organizing Map (SOM) has problems with some inactive neurons which have affected a result of clustering. In this study, we propose a new SOM algorithm which is the DisconnectingExpand
Neural Network and Self-organizing Maps
Self-organizing map (SOM) is a famous type of artificial neural network, which was first developed by Kohonen (1997). The SOM algorithm is vary practical and has many useful applications, such asExpand
Self-Organizing Maps for imprecise data
TLDR
This paper proposes an extension of the self-Organizing Maps for data imprecisely observed (SOMs-ID) based on two distances for imprecising data, and provides a simulation study and different substantive applications. Expand
Competing Behavior of Two Kinds of Self-Organizing Maps and Its Application to Clustering
TLDR
This paper proposes a method of simultaneously using two kinds of SOM whose features are different (the nSOM method), one is distributed in the area at which input data are concentrated, and the other self-organizes the whole of the input space. Expand
Reunifying Self-Organizing Map
The Self-Organizing Map (SOM) attracts attentions for clustering in these years. In this study, we propose a Reunifying Self-Organizing Map which is a new SOM algorithm. The initial state of allExpand
Self-Organizing Maps
TLDR
Through interplay of lateral inhibition and Hebbian learning within a localized region of a one-layered neural network, the network acquires a low-dimensional representation of high-dimensional input features, which respects topological relationships of the input space. Expand
...
1
2
3
4
5
...