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A self-organizing map (SOM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a.
Table of contents
- Self-organizing map
- The Ultimate Guide to Self Organizing Maps (SOM's)
- Kohonen Self-Organizing Feature Maps - Tutorialspoint
The distance between the input vector and the weights of node is calculated in order to find the BMU. The size of the neighborhood around the BMU is decreasing with an exponential decay function. It shrinks on each iteration until reaching just the BMU. The weight of every node within the neighborhood is adjusted, having greater change for neighbors closer to the BMU.www.npago.com/images/negro/levy-sexo-gay.php
The decay of learning rate is calculated for each iteration. As training goes on, the neighborhood gradually shrinks. At the end of training, the neighborhoods have shrunk to zero size. The influence rate shows amount of influence a node's distance from the BMU has on its learning. In the simplest form influence rate is equal to 1 for all the nodes close to the BMU and zero for others, but a Gaussian function is common too.
Finally, from a random distribution of weights and through much iteration, SOM is able to arrive at a map of stable zones. When Viscovery is used to evaluate dependences, to investigate properties of the data distribution, to search for clusters, or to monitor new data — just to mention a few options — an intuitive and inspiring interactive process emerges. In addition to the capabilities for data exploration, Viscovery employs a multitude of statistical techniques for the creation and application of classification and prediction models, all embedded in a workflow-guided project environment.
The Ultimate Guide to Self Organizing Maps (SOM's)
The Viscovery data mining products offer comprehensive technical features for the generation of predictive models, such as scoring models or segmentations, as well as their application and real-time integration into an operational environment. Much of the theoretical background as well as of innovative algorithms in the field of SOMs is owed to Prof.
Kohonen, who, as the former Head of the Laboratory of Computer and Information Science at the Helsinki University of Technology, prominently contributed to the creation, evolution, and spread of SOM technology. As the originator of several new concepts, Prof.
His manifold contributions to scientific progress have been multiply awarded and honored. Learn more about features and benefits of, and solutions using, Viscovery software.
Kohonen Self-Organizing Feature Maps - Tutorialspoint
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Learn more about Predictive analytics Integrating models Areas in which Viscovery is already solving problems. Self-organizing maps.
Analogy to regression The SOM method can be viewed as a non-parametric regression technique that converts multi-dimensional data spaces into lower dimensional abstractions. Coaction with other analytical methods In Viscovery, the data representation contained in the trained SOM is systematically converted for use across a broad spectrum of visualization techniques.
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