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Что (кто) такое self-organizing program - определение
Self-organizing strategy; Move-to-root heuristic; Self-organizing heuristic
Kohonen
![One-dimensional SOM versus principal component analysis (PCA) for data approximation. SOM is a red [[broken line]] with squares, 20 nodes. The first principal component is presented by a blue line. Data points are the small grey circles. For PCA, the [[fraction of variance unexplained]] in this example is 23.23%, for SOM it is 6.86%.<ref>Illustration is prepared using free software: Mirkes, Evgeny M.; [http://www.math.le.ac.uk/people/ag153/homepage/PCA_SOM/PCA_SOM.html ''Principal Component Analysis and Self-Organizing Maps: applet''], University of Leicester, 2011</ref>](https://commons.wikimedia.org/wiki/Special:FilePath/SOMsPCA.PNG?width=200 "One-dimensional SOM versus principal component analysis (PCA) for data approximation. SOM is a red [[broken line]] with squares, 20 nodes. The first principal component is presented by a blue line. Data points are the small grey circles. For PCA, the [[fraction of variance unexplained]] in this example is 23.23%, for SOM it is 6.86%.<ref>Illustration is prepared using free software: Mirkes, Evgeny M.; [http://www.math.le.ac.uk/people/ag153/homepage/PCA_SOM/PCA_SOM.html ''Principal Component Analysis and Self-Organizing Maps: applet''], University of Leicester, 2011</ref>")
![Cartographical representation of a self-organizing map ([[U-Matrix]]) based on Wikipedia featured article data (word frequency). Distance is inversely proportional to similarity. The "mountains" are edges between clusters. The red lines are links between articles.](https://commons.wikimedia.org/wiki/Special:FilePath/Self oraganizing map cartography.jpg?width=200 "Cartographical representation of a self-organizing map ([[U-Matrix]]) based on Wikipedia featured article data (word frequency). Distance is inversely proportional to similarity. The \"mountains\" are edges between clusters. The red lines are links between articles.")
TYPE OF ARTIFICIAL NEURAL NETWORK
Self organizing map; Kohonen map; Kohonen; Self Organizing Map; Self-organising map; Self-Organizing Map; Kohonen maps; Self Organising Map; Self-organising maps; Self organising map; Self organizing maps; Self-organizing nets; Self-organizing feature map; Kohonen network; SOFM; Time adaptive self-organizing map
Self-organizing map
TYPE OF ARTIFICIAL NEURAL NETWORK
Self organizing map; Kohonen map; Kohonen; Self Organizing Map; Self-organising map; Self-Organizing Map; Kohonen maps; Self Organising Map; Self-organising maps; Self organising map; Self organizing maps; Self-organizing nets; Self-organizing feature map; Kohonen network; SOFM; Time adaptive self-organizing map
A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data. For example, a data set with p variables measured in n observations could be represented as clusters of observations with similar values for the variables.
Self-organizing network
A self-organizing network (SON) is an automation technology designed to make the planning, configuration, management, optimization and healing of mobile radio access networks simpler and faster. SON functionality and behavior has been defined and specified in generally accepted mobile industry recommendations produced by organizations such as 3GPP (3rd Generation Partnership Project) and the NGMN (Next Generation Mobile Networks).
Википедия
Self-organising heuristic
In computing, a Self-organising heuristic is an algorithm that modifies a data structure such as a linked list in response to use of the data structure.
Examples might be:
- Move-to-front (or 'Move to top')
- Self-learning Frequency list (or 'Order by access frequency')
- Re-insert at random position
- Move to back
