self-organizing map

This includes matrices, continuous functions or even other self-organizing maps.

A self-organizing map consists of components called nodes or neurons.

A majority of the data analysis simulators on the market use backpropagating networks or self-organizing maps as their core.

The goal of learning in the self-organizing map is to cause different parts of the network to respond similarly to certain input patterns.

Self-organizing map, a type of artificial neural network in machine learning, also called Kohonen network.

Vector quantization is based on the 'competitive learning' paradigm, so it is closely related to the self-organizing map model.

The Self-Organizing Map projects high-dimensional input data onto a low dimensional (usually two-dimensional) space.

This learning equation was also used by Kohonen in his applications of Self-Organizing Maps starting in 1984.

Typical examples might include clustered results (from hierarchical or k-means clustering) or results from self-organizing maps.

The self-organizing map describes a mapping from a higher dimensional input space to a lower dimensional map space.

Self-organizing maps are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space.

The modeling components include neural networks, polynomials, locally-weighted Bayesian regression, k-means clustering, and self-organizing maps.

This can be done using data-driven methods, such as hierarchical clustering and self-organizing maps [ 1, 2], which identify groups of genes with similar expression patterns.

GHA is used in applications where a self-organizing map is necessary, or where a feature or principal components analysis can be used.

Self-organizing map for Haskell: An open-source implementation of a self-organising map in Haskell.

Self-organizing map (SOM)

Growing self-organizing map (GSOM)

This includes a central role in the development of the Websom method for visual information retrieval and text mining based on the Kohonen self-organizing map algorithm.

A variety of techniques such as hierarchical clustering, k -means clustering and self-organizing maps have been implemented with success, especially in classification [ 13].

The self-organizing map (SOM) invented by Teuvo Kohonen performs a form of unsupervised learning.

The time adaptive self-organizing map (TASOM) network is an extension of the basic SOM.

Workshop on Self-Organizing Maps (WSOM).

Among neural network models, the self-organizing map (SOM) and adaptive resonance theory (ART) are commonly used unsupervised learning algorithms.

One of the best known and most efficient neural network methods for achieving unsupervised clustering is the Self-Organizing Map (SOM).

DemoGNG Java applet which demonstrates neural gas, growing neural gas, self-organizing maps and other other methods related to competitive learning.