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Neural Networks, 2009
This paper proposes an extension of the self-organizing map (SOM), in which the mapping objects themselves are self-organizing maps. Thus a "SOM of SOMs" is presented, which we refer to as a SOM(2). A SOM(2) has a hierarchical structure consisting of a single parent SOM and a set of child SOMs. Each child SOM is trained to represent the distribution of
Tetsuo Furukawa
exaly +3 more sources
This paper proposes an extension of the self-organizing map (SOM), in which the mapping objects themselves are self-organizing maps. Thus a "SOM of SOMs" is presented, which we refer to as a SOM(2). A SOM(2) has a hierarchical structure consisting of a single parent SOM and a set of child SOMs. Each child SOM is trained to represent the distribution of
Tetsuo Furukawa
exaly +3 more sources
International Journal of Intelligent Systems Technologies and Applications, 2007
We propose a method to use the classification capabilities of self organising neural networks to extract symbolic information from raw data. The Multi-SOM (M-SOM) approach is a variant of Self Organising Maps (SOM). Multi-SOMS consist of a set of partner SOMs, trained simultaneously and in concurrence to each other, to adapt to different classes.
Nils Goerke +2 more
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We propose a method to use the classification capabilities of self organising neural networks to extract symbolic information from raw data. The Multi-SOM (M-SOM) approach is a variant of Self Organising Maps (SOM). Multi-SOMS consist of a set of partner SOMs, trained simultaneously and in concurrence to each other, to adapt to different classes.
Nils Goerke +2 more
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Neural Networks, 2006
Since the discovery of the SOM's by T. Kohonen, many results that provide a better description of their behaviour have been found. Most of them are very convincing, but from a mathematical point of view, only a few are actually proved. In this paper, we make a review of some results that are still to be proved and give some framework to formulate ...
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Since the discovery of the SOM's by T. Kohonen, many results that provide a better description of their behaviour have been found. Most of them are very convincing, but from a mathematical point of view, only a few are actually proved. In this paper, we make a review of some results that are still to be proved and give some framework to formulate ...
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Neurocomputing, 1998
Many applications of self-organizing maps (SOM) require high computing performance in order to be efficient. Because of the regular and modular structure of SOMs, a custom hardware realization is obvious. Based on the idea of a massively parallel system, several chips have been designed, manufactured and tested by the authors.
Rüping, Stefan +2 more
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Many applications of self-organizing maps (SOM) require high computing performance in order to be efficient. Because of the regular and modular structure of SOMs, a custom hardware realization is obvious. Based on the idea of a massively parallel system, several chips have been designed, manufactured and tested by the authors.
Rüping, Stefan +2 more
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1997
I introduce the T-SOM, an unsupervised neural network model based on well-known Kohonen Self-Organizing Maps. This model adds to SOM-properties the next new characteristics : a multiresolution knowledge representation, a low complexity algorithm and a simplified learning parameters tuning.
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I introduce the T-SOM, an unsupervised neural network model based on well-known Kohonen Self-Organizing Maps. This model adds to SOM-properties the next new characteristics : a multiresolution knowledge representation, a low complexity algorithm and a simplified learning parameters tuning.
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A GH-SOM optimization with SOM labelling and dunn index
2011 11th International Conference on Hybrid Intelligent Systems (HIS), 2011Clustering is an unsupervised classification method that divides a data set in groups, where the elements of a group have similar characteristics to each other. A well-known clustering method is the Growing Hierarchical Self-Organizing Map (GH-SOM), that improves the results of an ordinary SOM by controlling the number of neurons generated.
Alessandro Bokan Garay +2 more
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A behavioral study of learning in standard SOM and in S-SOM
2009 International Joint Conference on Neural Networks, 2009In this paper we establish the completion of a previously published work, in part by the same authors, in which we proposed a novel learning algorithm involving Self Organizing Map's (SOM) internal structure in the learning process. We present a statistical and a behavioral study of our proposed solution, and confirm its results on a breast cancer ...
Soukeina Ben Chikha, Kirmene Marzouki
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Pattern Recognition, 2000
The Kohonen self-organizing map (SOM),is a topology-preserving map that maps data from higher dimensions onto a (typically) two-dimensional grid of lattice points [1]. The aim of self-organization is to generate a topology-preserving mapping, where the neighborhood relations in the input space are preserved as well as possible, in the neighborhood ...
Vishwanathan, SVN, Murty, Narasimha M
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The Kohonen self-organizing map (SOM),is a topology-preserving map that maps data from higher dimensions onto a (typically) two-dimensional grid of lattice points [1]. The aim of self-organization is to generate a topology-preserving mapping, where the neighborhood relations in the input space are preserved as well as possible, in the neighborhood ...
Vishwanathan, SVN, Murty, Narasimha M
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Tekst som anledning, dialog som data, kontekst som forstyrrer
2010This paper sets off to discuss the meaning of context in collaborative knowledge production. The discussion will be unfolded in relation to an experiment of moving a concrete text from one collective context of analysis to another. Doing the unthinkable: Moving a text out of context to find out how context informs focus of analysis and explore the ...
Olesen, Birgitte Ravn; id_orcid 0000-0002-5982-2987 +1 more
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