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dc.date.accessioned 2012-11-01T21:38:16Z
dc.date.available 2012-11-01T21:38:16Z
dc.date.issued 2007
dc.identifier.uri http://hdl.handle.net/10915/23485
dc.description.abstract Ensemble methods show improved generalization capabilities that outperforrn those of single larners. lt is generally accepted that, for aggregation to be effective, the individual learners must be as accurate and diverse as possible. An important problem in ensemble learning is then how to find a good balance between these two conflicting conditions. For tree-based methods a successfill strategy was introduced by Breiman with the Random-Forest algorithm. In this work we introduce new methods for neural network ensemble construction that follow Random-Forest-like strategies to construct ensembles. Using several real and artificial regression problems, we compare onr new methods with the more typical Bagging algorithrm and with three state-of-the-art regression methods. We find that our algorithms produce very good results on several datasets. Some evidence suggest that our new methods work better on problems with several redundant or noisy inputs. en
dc.format.extent p. 1502-1512 es
dc.language en es
dc.title Random forest-like strategies for neural networks ensembles contruction en
dc.type Objeto de conferencia es
sedici.creator.person Namías, Rafael es
sedici.creator.person Granitto, Pablo Miguel es
sedici.subject.materias Ciencias Informáticas es
sedici.subject.materias Informática es
sedici.description.fulltext true es
mods.originInfo.place Red de Universidades con Carreras en Informática (RedUNCI) es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
sedici.date.exposure 2007-10
sedici.relation.event XIII Congreso Argentino de Ciencias de la Computación es
sedici.description.peerReview peer-review es
sedici.subject.acmcss98 Neural nets es
sedici.subject.acmcss98 Network communications es
sedici.subject.acmcss98 Network management es

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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)