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self organizing map
In this paper we study the sensitivity of the Self Organizing Map to several parameters in the context of the one-pass adaptive computation of cluster representatives over non-stationary data.
      
The Self Organizing Map (SOM) algorithm has been utilized, with much success, in a variety of applications for the automatic organization of full-text document collections.
      
The training set is fed to a self organizing map neural network to cluster the measurements.
      
In fact, a Self Organizing Map (SOM), combined with multiple recurrent neural networks (RNN) has been trained to predict the components of noisy and large data set.
      
Apart from that it uses the same colors as the self organizing map for displaying the main bar.
      
A self organizing map was used to cluster the user access records.
      
By clicking twice the self organizing map changes the currently selected node to the one corresponding to the chosen frame.
      
Clicking twice, it forces the self organizing map to change the currently selected node to the one corresponding to the chosen frame.
      
First the matrices are grouped automatically by an unsupervised clustering approach based on a self organizing map algorithm.
      
It contains a visual representation of a two-dimensional self organizing map where the clusters are represented by hexagonal nodes.
      
It is these context vectors that are given to the self organizing map for visualization.
      
In this paper, we propose to speed up the self organizing map on dissimilarity data for large data sets.
      
Section 2 presents an overview of both context vectors and the Self Organizing Map.
      
Section II explains the concepts and the algorithm of Self Organizing Map.
      
Self organizing map and symbolic data, Journal of Symbolic Data Analysis, 2.
      
Section 3 presents the usage based dissimilarity used to compare prior document clusters as well as the Median Self Organizing Map.
      
The vectors were then input to a Self Organizing Map of 9 neurons organized as a 3 x 3 grid.
      
These algorithms use the self organizing map described above and expert knowledge to derive the most suitable query model for a search image.
      
The main bar is a projection of the colours of the self organizing map into the temporal axis.
      
The last stage uses a self organizing map neural network for classifying the candidate regions as either face or non-face.
      
 

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