Self-Organizing Map - Factbites

Self-Organizing Map - Factbites
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Topic: Self-Organizing Map

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	Self-organizing map - Wikipedia, the free encyclopedia
		The self-organizing map (SOM) is a method for unsupervised learning, based on a grid of artificial neurons whose weights are adapted to match input vectors in a training set.
		Output maps can also be made in different dimensions: 1-dimensional, 2-dimensional, etc., but most popular are 2D and 3D maps, for SOMs are mainly used for dimensionality reduction rather than expansion.
		It was first described by the Finnish professor Teuvo Kohonen and is thus sometimes referred to as a Kohonen map.
	en.wikipedia.org /wiki/Self_organizing_map (722 words)

	Kohonen's Self-Organizing Map (SOM)
		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.
		Kohonon's SOMs are a type of unsupervised learning.
	www.willamette.edu /~gorr/classes/cs449/Unsupervised/SOM.html (440 words)

	Self-Organizing Maps Applet
		This is a demonstration of how Self-Organizing Maps work.
		Below this is the option of viewing the actual colored SOM and the SOM displaying the similarities and differences in the map as described determining the quality of the map.
		Assuming SOMs work, the final picture produced should have similar colors close to each and when the “Similarity SOM” option is toggled, there should be black lines separating colors which are not similar to each other.
	davis.wpi.edu /%7Ematt/courses/soms/applet.html (188 words)

	Self-Organizing Map Training Visualization
		A self-organizing map is trained with a method that is called competition learning: When an input pattern is presented to the network, that neuron in the competition layer is determined, the reference vector of which is closest to the input pattern.
		These three-dimensional pattern regions may be somewhat better suited to demonstrate the idea of self-organizing maps, because people tend to be confused by the fact that for the other three example the input space and the map have the same number of dimensions.
		That is, a self-organizing map basically represents a set of vectors in the input space: one vector for each neuron in the competition layer.
	fuzzy.cs.uni-magdeburg.de /~borgelt/doc/somd (1952 words)

	Landuse Mapping Using Self Organizing Map
		Self Organizing Map (SOM) is a process of unsupervised learning whereby the significant patterns or features in the input are discovered [3,4].
		In the neural area, it is said Self organized because the process of adaptive modify weights as a learning process is done by Maps itself according to the condition input which is applied.
		SOM uses the Euclidean distance of each input data and weight neuron to refine weight neuron (cluster center) while ISODATA calculate the mean and standard deviation.
	www.ess.co.at /GAIA/CASES/TAI/taipaper.html (2740 words)

	Automatic Labeling of Self-Organizing Maps:Making a Treasure-Map Reveal its Secrets
		Without any additional knowledge on the underlying documents, the resulting mapping of the SOM given in Figure 2 is hard to interpret, although the names of the authors may give some hints towards the cluster structure of the map (at least if you know the authors and have some knowledge concerning their research areas).
		The resulting labeled map allows the user to better understand the structure and the information available in the map and the reason for a specific map organization, especially when only little prior information on the data set and its characteristics is available.
		This serves as a description for each set of data mapped onto a node.
	www.ifs.tuwien.ac.at /ifs/research/pub_html/rau_pakdd99/rau_pakdd99.html (3710 words)

	Self-Organizing Map (SOM)
		The Self-Organizing Map is based on unsupervised learning, which means that no human intervention is needed during the learning and that little needs to be known about the characteristics of the input data.
		The Self-Organizing Map was developed by professor Kohonen [20].
		Map units, or neurons, usually form a two-dimensional lattice and thus the mapping is a mapping from high dimensional space onto a plane.
	www.cis.hut.fi /~jhollmen/dippa/node9.html (520 words)

	The Self Organizing Map: Unsupervised Competitive Learning
		The Self Organizing Map is called a competitive algorithm because units compete to represent the input pattern.
		Self organizing networks like the SOM have been used as models of how sensory maps form within human and animal brains.
		Another way to look at the Self Organizing Map is to plot the weight vector associated with each of the map units in the input space.
	lsa.colorado.edu /~simon/cmc/chapters/SOM/index2.html (1539 words)

	Introduction to Self-Organizing Map
		Neighborhood relation: The neurons of the map are connected to adjacent neurons by a neighborhood relation dictating the structure of the map.
		In the SOM similar vectors in the input space are projected onto nearby neurons on the map.
		In this respect the SOM is a multidimensional scaling methods which project data from input space to a lower-dimensional output space.
	www.csc.fi /cschelp/sovellukset/math/matlab/cedar/somalg.html (1045 words)

	Self Organizing Map (SOM) in Excel
		On the map, ideally one should represent all the data points of a cluster by a single point - the neuron that captured all those points.
		SOM is an extensively researched area and it has been applied sucessfully on problems from various fields.
		You want to buy a commercial SOM software eventually but for now you want to have a feel of it and see what kind of features would be useful or nice to have.
	www.geocities.com /adotsaha/NN/SOMinExcel.html (1418 words)

	Self-Organizing Maps
		Self-organizing maps (SOMs) are a data visualization technique invented by Professor Teuvo Kohonen which reduce the dimensions of data through the use of self-organizing neural networks.
		The way SOMs go about reducing dimensions is by producing a map of usually 1 or 2 dimensions which plot the similarities of the data by grouping similar data items together.
		SOMs organize sample data so that in the final product, the samples are usually surrounded by simliar samples, however similar samples are not always near each other.
	davis.wpi.edu /~matt/courses/soms (2776 words)

	Self-Organizing Feature Map for Multi-Spectral Spot Land Cove Classification
		In this article, a window mask is used to extract texture pattern features, and a self-organizing feature maps (SOFM) is proposed to learn the pattern similarity in the feature space, then a Learning Vector Quantization (LVQ) network with the trained SOFM as its hidden layer is used to classified the test images.
		In a topology-preserving map, neurons located physically next to each other will respond to input vectors of classes that are likewise next to each other.
		SOFM based on competitive learning is a topology-preserving map (Kohonen, 1982, 84), and can be adjusted to approach the probability distribution of the inputs (Zheng, 1996).
	www.gisdevelopment.net /aars/acrs/2000/ts9/imgp0024pf.htm (1263 words)

	Self-Organizing Maps
		"In this paper we present the growing hierarchical self-organizing map.
		SOMs is one in which the system of relation and presentation itself is generated from the data encountered - data-driven form as well as data-driven content.
		Hence a map of total annual rainfall in the United States is data-driven if it is supplied from a database on the server, and changes dynamically as the data is updated or revised.
	www.english.ucsb.edu /grad/student-pages/jdouglass/coursework/hyperliterature/soms (4336 words)

	Cogprints - Modeling the development of lexicon with a growing self-organizing map
		During growth, new nodes are inserted in order to reduce the map quantization error, and the insertion occurs only to yet unoccupied grid positions, thus preserving the 2D map topology.
		The model is initialized with a subset of units in GLM and a subset of the lexicon, which enables it to capture the regularities of the input space and decrease chances of catastrophic interference.
		Implications of the model are discussed with respect to language acquisition by children.
	cogprints.org /2150 (244 words)

	Neural Networks Information Homepage
		The Self-Organizing Map (SOM) was introduced by Teuvo Kohonen in 1982.
		The SOM is quite a unique kind of neural network in the sense that it constructs a topology preserving mapping from the high-dimensional space onto map units in such a way that relative distances between data points are preserved.
		The SOM (also known as the Kohonen feature map) algorithm is one of the best known artificial neural network algorithms.
	koti.mbnet.fi /~phodju/nenet/SelfOrganizingMap/General.html (233 words)

	A Scalable Self-organizing Map Algorithm
		The value of the mapping node vector is then adjusted to reduce the Euclidean distance.
		Another statistical property of Kohonen's SOM is that the density of nodes in the vector space resembles the distribution density of input vectors in the concept space.
		A sketch of a revised SOM algorithm for textual classification [
	ai.bpa.arizona.edu /go/intranet/papers/A_Scalable-98.htm (7839 words)

	A Self-Organizing Cyberspace Using Human-Generated Hop Data
		While there have been a number of other research efforts into the mapping of a self-organizing cyberspace since the boom of the WWW, the approach of using human-generated "hop data" is unique and innovative.
		The objective of this proposal is to create a 3-D visual map of Internet resources, a cyberspace, that is self-organized by the traffic patterns of users over time to reveal the natural relationships between those data sources.
		As the density mapping is generated by human traffic, it is our expectation that non-obvious and hidden relationships between Internet resources will be revealed that would normally evade artificial intelligent agents.
	www.alumni.caltech.edu /~croft/research/internet/cyberspace (1188 words)

	Kohonen Self Organizing Map
		Kohonen self organizing maps are a way of exploring multidimensional spaces,looking for relationships which otherwise would be hidden from us.
		Using a Kohonen SOM is similar to using the basic competitive network, with some extra settings associated with the neighbourhood concept.
		Note that the Kohonen SOM not only categorizes the input data, it recognizes which input patterns are close to each other.
	www.ryerson.ca /~dgrimsha/courses/cps721/kohonen.html (1704 words)

	helpfulMed.com
		When the map no longer changes, the documents related to the terms in your browsing path will be displayed in your Browser window.
		Select a category that interests you; you may click on the map region or the phrase in the hierarchical list in the left panel.
		Use Medical Map to browse categories of medical journal literature.
	ai.bpa.arizona.edu /helpfulmed/visual.html (80 words)

	A Map of Yahoo! - Mappa.Mundi Magazine - Map of the Month
		Those who are interested in more technical detail on how ET-Map was created may wish to consult the research paper "Internet Categorization and Search: A Self-Organizing Approach" by Hsinchun Chen, Chris Schuffels, and Rich Orwig, 1996.
		The goal of information maps, like ET-Map, is to provide the browser with a sense of the lie of the information landscape, what is where, the location of clusters and hotspots, what is related to what.
		The map is a two-dimensional, multi-layered category map; its aim is to provide an intuitive visual information browsing tool.
	mappa.mundi.net /maps/maps_009 (1385 words)

	DYNAMICAL SYSTEMS THEORY: a Relevant Framework for Performance-Oriented Sports Biomechanics Research
		Topological clustering of patients using a self organizing neural map.
		Instead of measuring the “distance” between performances in the high dimensional input space, the neighborhood preservation properties of self-organizing maps enable the investigator to measure more effectively the distance between performances in the low dimensional output space.
		Self-organizing maps for the analysis of complex movement patterns.
	www.sportsci.org /jour/03/psg.htm (3472 words)

	Schoolen
		Kohonen Self-Organizing Maps A Self-Organizing Map (SOM) is an exploratory...
		Specifically, the analysis employs Kohonen maps to seek structures in a set of 21 macroeconomic...
		Kohonen maps are known to be topology preserving, so...
	low-saxon.encyclopedia.st /Schoolen (391 words)

	The Self-Organizing Map
		This chapter contains a brief introduction to the topic of Kohonen's Self-Organizing Maps, i.e.
		The chapter will be concluded with a discussion on the capabilities of this type of neural network as well as on some shortcomings to be addressed.
		Following a short presentation of the principles of the approach a description of the basic model will be given detailing the architecture and the learning-process of SOM.
	www.ifs.tuwien.ac.at /ifs/research/pub_html/rau_masterth96/node7.html (67 words)

	m.i.t. press - page 1
		i of the vortex : from neurons to self - LLINAS
	www.scientific-bookshop.com /m-i-t-press-1.htm (324 words)

	Self-Organizing Map research
		The research activities of the group include applications of the Self-Organizing Map (SOM) and Learning Vector Quantizing (LVQ) algorithms of Academician
		Typical applications are visualization of process states or financial results by representing the central dependencies within the data on the map.
		The SOM is an algorithm used to visualize and interpret large high-dimensional data sets.
	www.cis.hut.fi /research/som-research (183 words)

	Self-Organizing Systems FAQ for Usenet newsgroup comp.theory.self-org-sys
		Cybernetics is the precursor of complexity thinking in the investigation of dynamic systems and set the groundwork for the study of self-maintaining systems, using feedback and control concepts.
		organization) of interest to us are internal to the system, resulting from the interactions among the components and usually independent of the physical nature of those components.
		Thus organization does not 'violate' the 2nd Law (as often claimed) but seems to be a direct result of it.
	www.calresco.org /sos/sosfaq.htm (8398 words)

	generation5 - Self Organizing Map AI for Pictures
		NOTE the SOM map can also be in a 2D honeycomb shape or only have 1 Dimension.
		when i clicked on a Node of the SOM map (the maroon grid), then the thumbnail images associated to that Node were displayed to the ListView on the right.
		the SOM map was of size 10x10 and initialized with a gradient fill.
	www.generation5.org /content/2004/aiSomPic.asp (3956 words)

	Semantic Research for Digital Libraries
		Automatic Categorization: A category map is the result of performing a neural network based clustering (self-organizing) of similar documents followed by automatic category labeling.
		Specifically, the AI lab has developed a noun phrasing technique for concept extraction, a concept space technique for building automatic thesauri, and a self-organizing map (SOM) algorithm for building category maps.
		Examples include concept spaces and category maps in the Illinois project [12] and word sense disambiguation in the Berkeley project [14], voice recognition in the Carnegie Mellon project [13], and image segmentation and clustering in the project at the University of California at Santa Barbara [6].
	www.dlib.org /dlib/october99/chen/10chen.html (2870 words)

	Self-Organizing Feature Maps
		Self-organizing feature maps (SOFM) transform the input of arbitrary dimension into a one or two dimensional discrete map subject to a topological (neighborhood preserving) constraint.
		The feature maps are computed using Kohonen unsupervised learning.
		Hence the underlying structure of the input space is kept, while the dimensionality of the space is reduced.
	www.nd.com /models/sofm.htm (95 words)

	Bayesian Self-Organizing Map Simulation using Java Applet
		The Bayesian self-organizing map (BSOM) is a method for estimating a probability distribution generating data points on the basis of a Bayesian stochastic model.
		Utsugi (1996) ``Topology selection for self-organizing maps", Network: Computation in Neural Systems, vol.
		This applet searches for the maximum a posteriori (MAP) estimates of the centroid parameters using an expectation-maximization (EM) algorithm.
	staff.aist.go.jp /utsugi-a/Lab/BSOM1 (717 words)

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