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dc.contributor.author | Quintián Pardo, Héctor | |
dc.contributor.author | Corchado Rodríguez, Emilio Santiago | |
dc.date.accessioned | 2017-09-05T10:59:09Z | |
dc.date.available | 2017-09-05T10:59:09Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE. Volumen 59, pp. 218–235. Elsevier. | |
dc.identifier.issn | 0952-1976 | |
dc.identifier.uri | http://hdl.handle.net/10366/134269 | |
dc.description.abstract | In this study we present a novel version of the Scale Invariant Map (SIM) called Beta-SIM, developed to facilitate the clustering and visualization of the internal structure of complex datasets effectively and efficiently. It is based on the application of a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution, when applied to the Scale Invariant Map. The Beta-SIM behavior is thoroughly analyzed and successfully demonstrated over 2 artificial and 16 real datasets, comparing its results, in terms of three performance quality measures with other well-known topology preserving models such as Self Organizing Maps (SOM), Scale Invariant Map (SIM), Maximum Likelihood Hebbian Learning-SIM (MLHL-SIM), Visualization Induced SOM (ViSOM), and Growing Neural Gas (GNG). Promising results were found for Beta-SIM, particularly when dealing with highly complex datasets. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.publisher | Elsevier | |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Unported | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/ | |
dc.subject | Computer Science | |
dc.title | Beta Scale Invariant Map | |
dc.type | info:eu-repo/semantics/article | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess |
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