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• 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 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 model was first described as an artificial neural network by the Finnish professor Teuvo Kohonen, and is sometimes called a Kohonen map or network.Like 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 is 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 and 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 weka.
• Self-organizing_map wikiPageExternalLink ifcsoft.
• 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 wikiPageID "76996".
• Self-organizing_map wikiPageRevisionID "605892420".
• Self-organizing_map hasPhotoCollection Self-organizing_map.
• Self-organizing_map subject Category:Data_clustering_algorithms.
• Self-organizing_map subject Category:Dimension_reduction.
• Self-organizing_map subject Category:Neural_networks.
• Self-organizing_map type Abstraction100002137.
• Self-organizing_map type Cognition100023271.
• Self-organizing_map type Communication100033020.
• Self-organizing_map type ComputerArchitecture106725249.
• Self-organizing_map type DataClusteringAlgorithms.
• Self-organizing_map type Datum105816622.
• Self-organizing_map type Description106724763.
• Self-organizing_map type Information105816287.
• Self-organizing_map type Message106598915.
• Self-organizing_map type NeuralNetwork106725467.
• Self-organizing_map type NeuralNetworks.
• Self-organizing_map type PsychologicalFeature100023100.
• Self-organizing_map type Specification106725067.
• Self-organizing_map type Statement106722453.
• Self-organizing_map comment "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_map label "Carte auto adaptative".
• Self-organizing_map label "Mapa autoorganizado".
• Self-organizing_map label "Mapas de Kohonen".
• Self-organizing_map label "Selbstorganisierende Karte".
• Self-organizing_map label "Self-Organizing Map".
• Self-organizing_map label "Self-organizing map".
• Self-organizing_map label "Sieć Kohonena".
• Self-organizing_map label "Самоорганизующаяся карта Кохонена".
• Self-organizing_map label "شبكات كوهونين ذاتية التنظيم".
• Self-organizing_map label "自己組織化写像".
• Self-organizing_map sameAs Selbstorganisierende_Karte.
• Self-organizing_map sameAs Mapa_autoorganizado.
• Self-organizing_map sameAs Carte_auto_adaptative.
• Self-organizing_map sameAs Self-Organizing_Map.
• Self-organizing_map sameAs 自己組織化写像.
• Self-organizing_map sameAs 자기조직화지도.
• Self-organizing_map sameAs Sieć_Kohonena.
• Self-organizing_map sameAs Mapas_de_Kohonen.
• Self-organizing_map sameAs m.0k6rk.
• Self-organizing_map sameAs Q1136838.
• Self-organizing_map sameAs Q1136838.
• Self-organizing_map sameAs Self-organizing_map.
• Self-organizing_map wasDerivedFrom Self-organizing_map?oldid=605892420.
• Self-organizing_map depiction Synapse_Self-Organizing_Map.png.
• Self-organizing_map isPrimaryTopicOf Self-organizing_map.