Self-organizing map
From The Art and Popular Culture Encyclopedia
|
Related e |
|
Featured: |
A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map, and is therefore a method to do dimensionality reduction. Self-organizing maps differ from other artificial neural networks as they apply competitive learning as opposed to error-correction learning (such as backpropagation with gradient descent), and in the sense that they use a neighborhood function to preserve the topological properties of the input space.
[edit]
See also
- Neural gas
- Liquid state machine
- Hybrid Kohonen SOM
- Sparse coding
- Sparse distributed memory
- Deep learning
- Neocognitron
- Topological data analysis
Unless indicated otherwise, the text in this article is either based on Wikipedia article "Self-organizing map" or another language Wikipedia page thereof used under the terms of the GNU Free Documentation License; or on research by Jahsonic and friends. See Art and Popular Culture's copyright notice.
