dc.contributor.author | De Melo, Filipe M. | |
dc.contributor.author | Bertrand, Patrice | |
dc.contributor.author | De A. T. De Carvalho, Francisco | |
dc.date.accessioned | 2012-07-04T13:46:54Z | |
dc.date.available | 2012-07-04T13:46:54Z | |
dc.date.issued | 2012 | |
dc.identifier.uri | https://basepub.dauphine.fr/handle/123456789/9692 | |
dc.language.iso | en | en |
dc.subject | Adaptive distances | en |
dc.subject | City-block distances | en |
dc.subject | Interval-valued data | en |
dc.subject | Self-organizing maps | en |
dc.subject.ddc | 519 | en |
dc.title | Batch self-organizing maps based on city-block distances for interval variables | en |
dc.type | Document de travail / Working paper | |
dc.contributor.editoruniversityother | Centro de Informática (CIN) Universidade Federal de Pernambuco;Brésil | |
dc.contributor.editoruniversityother | Centro de Informatica UFPE (CIn) http://www2.cin.ufpe.br/site/index.php Universidade Federal de Pernambuco;Brésil | |
dc.description.abstracten | The Kohonen Self Organizing Map (SOM) is an unsupervised neural network method with a competitive learning strategy which has both clustering and visualization properties. Interval-valued data arise in practical situations such as recording monthly interval temperatures at meteorological stations, daily interval stock prices, etc. Batch SOM algorithms based on adaptive and non-adaptive city-block distances, suitable for objects described by interval-valued variables, that, for a fixed epoch, optimizes a cost function, are presented. The performance, robustness and usefulness of these SOM algorithms are illustrated with real interval-valued data sets. | en |
dc.publisher.name | Université Paris-Dauphine | en |
dc.publisher.city | Paris | en |
dc.identifier.citationpages | 15 | en |
dc.identifier.urlsite | http://hal.archives-ouvertes.fr/hal-00706519 | en |
dc.description.sponsorshipprivate | oui | en |
dc.subject.ddclabel | Probabilités et mathématiques appliquées | en |