Perry, D. A. 1995. Self-organizing systems across scales. Trends in Evolution and Ecology 10:241-244.
Self-Organizing Nets
By James Matthews
After a detailed look at supervised networks (see Perceptrons, Back-propagation and Associative Networks) we should look at a good example of unsupervised networks.
Kohonen's Self-Organizing Map (SOM)
Kohonon's SOMs are a type of unsupervised learning. The goal is to discover some underlying structure of the data.
Self-organizing map
The Self-organizing map (SOM), sometimes referred to as "Kohonen map" due to its invention by Professor Teuvo Kohonen, ...
Self-Organizing Maps and Learning Vector Quantization for Feature Sequences, Somervuo and Kohonen. 2004 (pdf)
Classification of Textual Documents using LVQ, Fahad and Sikander. 2007 (pdf)
[edit] External links ...
A self-organizing ANN (often called a Kohonen after its inventor) is exposed to large amounts of data and tends to discover patterns and relationships in that data. Researchers often use this type to analyze experimental data.
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SOFMs (Self-Organizing Feature Maps; Kohonen Networks). Neural networks based on the topological properties of the human brain, also known as Kohonen Networks (Kohonen, 1982; Fausett, 1994,; Haykin, 1994; Patterson, 1996).
Chapter 9 Self-Organizing Maps 425
9.1 Introduction 425
9.2 Two Basic Feature-Mapping Models 426
9.3 Self-Organizing Map 428
9.4 Properties of the Feature Map 437
9.5 Computer Experiments I: Disentangling Lattice Dynamics Using SOM 445
9.
It’s going to be self-organizing. ... [Q] What’s your definition of artificial intelligence?
Limitations of space preclude detailed discussion here of theories of self-organizing neural nets, and other models based on brain analogies. Several of these are described or cited in [C] and [D].
The model uses two learning algorithms: Kohonen Self-Organizing Feature Map and backpropagation algorithm. To perform the tasks, it has two modules and a retina for input.
Difference # 9: The brain is a self-organizing system
This point follows naturally from the previous point - experience profoundly and directly shapes the nature of neural information processing in a way that simply does not happen in traditional ...
He has developed a self-organizing network, sometimes called an auto-associator, that learns without the benefit of knowing the right answer. It is an unusual looking network in that it contains one single layer with many connections.
There are no connections within a layer. Self-organizing: A network is called self-organizing if it is capable of changing its connections so as to produce useful responses for input patterns without the instruction of a smart teacher.
Another criticism of the Open Source movement is that these projects are not really as self-organizing as their proponents claim.
This is a fairly easy to follow paper that shows the transformations that have to be done for financial data (currency fluctuations) including the use of a self-organizing map.
assigns the highest probability to the data after all parameters have been integrated out. See my research demo page. Also see "Bayesian Interpolation", "Automatic choice of dimensionality for PCA", "Hyperparameter Selection for Self-Organizing Maps", ...
In the next section, we shall formalize some of the definitions presented in this section, and shall look at the mathematics of the topology-preserving map. Henceforth, we shall refer to the topology-preserving map as a self-organizing map (SOM).
 See also: Neural network, Percept, Perceptron, Classification, Knowledge
  
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