Generative topographic map (GTM) is a machine learning method that is a probabilistic counterpart of the selforganizing map (SOM), is probably convergent and does not require a shrinking neighborhood or a decreasing step size. It is a generative model: the data is assumed to arise by first probabilistically picking a point in a lowdimensional space, mapping the point to the observed highdimensional input space (via a smooth function), then adding noise in that space. The parameters of the lowdimensional probability distribution, the smooth map and the noise are all learned from the training data using the expectationmaximization (EM) algorithm. GTM was introduced in 1996 in a paper by Christopher Bishop, Markus Svensen, and Christopher K. I. Williams.
Property  Value 

dbo:abstract 

dbo:wikiPageEditLink  
dbo:wikiPageExternalLink  
dbo:wikiPageExtracted 

dbo:wikiPageHistoryLink  
dbo:wikiPageID 

dbo:wikiPageLength 

dbo:wikiPageModified 

dbo:wikiPageOutDegree 

dbo:wikiPageRevisionID 

dbo:wikiPageRevisionLink  
dbp:wikiPageUsesTemplate  
dct:subject  
rdf:type  
rdfs:comment 

rdfs:label 

owl:sameAs  
foaf:isPrimaryTopicOf  
is dbo:wikiPageDisambiguates of  
is dbo:wikiPageRedirects of  
is foaf:primaryTopic of 