Kohonen's SOM is called a topology-preserving map because there is a topological structure imposed on the nodes in the network. A topological map is simply a mapping that preserves neighborhood relations.
The SOM Learning Algorithm
During the training period, each unit with a positive activity within the neighborhood of the winning unit participates in the learning process.We can describe the learning process by the equations: ...
Kohonen SOM
the AI technique to be used is a type of Neural Network (NN). i've written one other NN article : /aiTabletOcr. that article uses the most popular type of NN called Back Propagation.
The SOM was created as a biological model of neurons and is a heuristic algorithm. By contrast, the GTM has nothing to do with neuroscience or cognition and is a probabilistically principled model.
There are several points in SOM that suggest that commonsense reasoning systems may not need to increase in the density of physical connectivity as fast as they increase the complexity and scope of their performances.
Now that we know how the SOM algorithm works we are having a look at how we can use it for color recognition. Every color can be understood as a vector of three components (red, green, blue) well use a color just like a point in 3d space.
5 Computer Experiments I: Disentangling Lattice Dynamics Using SOM 445
9.6 Contextual Maps 447
9.7 Hierarchical Vector Quantization 450
9.8 Kernel Self-Organizing Map 454
9.9 Computer Experiment II: Disentangling Lattice Dynamics Using Kernel SOM 462 ...
The Self-organizing map (SOM), sometimes referred to as "Kohonen map" due to its invention by Professor Teuvo Kohonen, is an unsupervised learning technique that reduces the dimensionality of data through the use of a self-organizing neural network.
More advanced algorithms related to k means are Expected Maximization (EM) algorithm especially Gaussian Mixture, Self-Organization Map (SOM) from Kohonen, Learning Vector Quantization (LVQ).
Note that a very special type of Neural Networks, known as Kohonen Maps (or "SOM") may be perceived as a partitioning technique that attempts to give a 2-D representation of the classes that preserves their relative positions.
In this paper we investigate the Self-Organizing Map (SOM), whose role is to perform just this kind of task; in other words, to cluster data samples so as to reveal the relationships that exist among them." ...
 See also: Neural network, Self-organizing, Self-organizing map, Classification, Knowledge
  
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