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• W2011133372 abstract "We present a self-organizing map model to study qualitative data (also called categorical data). It is based on a probabilistic framework which does not assume any prespecified distribution of the input data. Stochastic approximation theory is used to develop a learning rule that builds an approximation of a discrete distribution on each unit. This way, the internal structure of the input dataset and the correlations between components are revealed without the need of a distance measure among the input values. Experimental results show the capabilities of the model in visualization and unsupervised learning tasks." @default.
• W2011133372 created "2016-06-24" @default.
• W2011133372 creator A5091243468 @default.
• W2011133372 date "2010-12-01" @default.
• W2011133372 modified "2023-09-30" @default.
• W2011133372 title "Probabilistic self-organizing maps for qualitative data" @default.
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• W2011133372 doi "https://doi.org/10.1016/j.neunet.2010.07.002" @default.
• W2011133372 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20674268" @default.
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