Matches in DBpedia 2015-10 for { <http://dbpedia.org/resource/Self-organizing_map> ?p ?o }
- Self-organizing_map abstract "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. Self-organizing maps are different 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. This makes SOMs useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The artificial neural network introduced by the Finnish professor Teuvo Kohonen in the 1980s is sometimes called a Kohonen map or network. The Kohonen net is a computationally convenient abstraction building on work on biologically neural models from the 1970s and morphogenesis models dating back to Alan Turing in the 1950sLike most artificial neural networks, SOMs operate in two modes: training and mapping. "Training" builds the map using input examples (a competitive process, also called vector quantization), while "mapping" automatically classifies a new input vector.A self-organizing map consists of components called nodes or neurons. Associated with each node are a weight vector of the same dimension as the input data vectors, and a position in the map space. The usual arrangement of nodes is a two-dimensional regular spacing in a hexagonal or rectangular grid. The self-organizing map describes a mapping from a higher-dimensional input space to a lower-dimensional map space. The procedure for placing a vector from data space onto the map is to find the node with the closest (smallest distance metric) weight vector to the data space vector.While it is typical to consider this type of network structure as related to feedforward networks where the nodes are visualized as being attached, this type of architecture is fundamentally different in arrangement and motivation.Useful extensions include using toroidal grids where opposite edges are connected and using large numbers of nodes.It has been shown that while self-organizing maps with a small number of nodes behave in a way that is similar to K-means, larger self-organizing maps rearrange data in a way that is fundamentally topological in character.It is also common to use the U-Matrix. The U-Matrix value of a particular node is the average distance between the node's weight vector and that of its closest neighbors. In a square grid, for instance, we might consider the closest 4 or 8 nodes (the Von Neumann and Moore neighborhoods, respectively), or six nodes in a hexagonal grid.Large SOMs display emergent properties. In maps consisting of thousands of nodes, it is possible to perform cluster operations on the map itself.".
- Self-organizing_map thumbnail Synapse_Self-Organizing_Map.png?width=300.
- Self-organizing_map wikiPageExternalLink ai4r.org.
- Self-organizing_map wikiPageExternalLink kohonen.
- Self-organizing_map wikiPageExternalLink som.
- Self-organizing_map wikiPageExternalLink som.
- Self-organizing_map wikiPageExternalLink ifcsoft.
- Self-organizing_map wikiPageExternalLink weka.
- Self-organizing_map wikiPageExternalLink somoclu.
- Self-organizing_map wikiPageExternalLink supraHex.
- Self-organizing_map wikiPageExternalLink www.demogng.de.
- Self-organizing_map wikiPageExternalLink nh2014168.htm.
- Self-organizing_map wikiPageExternalLink programs.
- Self-organizing_map wikiPageExternalLink php-som.
- Self-organizing_map wikiPageExternalLink SOMPY.
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- Self-organizing_map wikiPageRevisionID "683468521".
- Self-organizing_map wikiPageWikiLink Alan_Turing.
- Self-organizing_map wikiPageWikiLink Approximation_error.
- Self-organizing_map wikiPageWikiLink Artificial_neural_network.
- Self-organizing_map wikiPageWikiLink Backpropagation.
- Self-organizing_map wikiPageWikiLink Bootstrap_sampling.
- Self-organizing_map wikiPageWikiLink Bootstrapping_(statistics).
- Self-organizing_map wikiPageWikiLink Category:Artificial_neural_networks.
- Self-organizing_map wikiPageWikiLink Category:Data_clustering_algorithms.
- Self-organizing_map wikiPageWikiLink Category:Dimension_reduction.
- Self-organizing_map wikiPageWikiLink Category:Finnish_inventions.
- Self-organizing_map wikiPageWikiLink Cerebral_cortex.
- Self-organizing_map wikiPageWikiLink Competitive_Hebbian_Learning.
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- Self-organizing_map wikiPageWikiLink Euclidean_distance.
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- Self-organizing_map wikiPageWikiLink File:SOM_of_Fishers_Iris_flower_data_set.JPG.
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- Self-organizing_map wikiPageWikiLink Rectangle.
- Self-organizing_map wikiPageWikiLink Rectangular.
- Self-organizing_map wikiPageWikiLink Resampling_(statistics).
- Self-organizing_map wikiPageWikiLink Scientific_visualization.
- Self-organizing_map wikiPageWikiLink Self-organizing_map.
- Self-organizing_map wikiPageWikiLink Sense.
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- Self-organizing_map wikiPageWikiLink Teuvo_Kohonen.
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- Self-organizing_map wikiPageWikiLink File:SOMsPCA.PNG.
- Self-organizing_map wikiPageWikiLink File:Somtraining.svg.
- Self-organizing_map wikiPageWikiLink File:Synapse_Self-Organizing_Map.png.
- Self-organizing_map wikiPageWikiLinkText "Hebbian model".
- Self-organizing_map wikiPageWikiLinkText "Kohonen network".
- Self-organizing_map wikiPageWikiLinkText "Kohonen neural network".
- Self-organizing_map wikiPageWikiLinkText "SOM or Kohonen".
- Self-organizing_map wikiPageWikiLinkText "SOM".
- Self-organizing_map wikiPageWikiLinkText "Self Organizing Map (Kohonen)".
- Self-organizing_map wikiPageWikiLinkText "Self Organizing Map".
- Self-organizing_map wikiPageWikiLinkText "Self-organizing Map".
- Self-organizing_map wikiPageWikiLinkText "Self-organizing map (SOM)".