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dc.date.accessioned 2019-12-20T14:02:43Z
dc.date.available 2019-12-20T14:02:43Z
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/87801
dc.description.abstract In this work, we present two new variants to the deepSOM model: the deep elastic SOM (deSOM) and the deep ensemble elastic SOM (deeSOM), which overcome the mentioned issues. In deSOM the number of deep levels not only grows automatically, but also the size of each layer is expanded adaptively according to the data at each level, thus pre-miRNA neurons can be re-organized in a larger space. en
dc.format.extent 1-4 es
dc.language en es
dc.subject Bioinformatics es
dc.subject Pre-miRNA classification es
dc.subject Deep neural architectures es
dc.subject High class imbalance es
dc.title Deep neural architectures for highly imbalanced data in bioinformatics en
dc.type Objeto de conferencia es
sedici.identifier.issn 2683-8966 es
sedici.creator.person Bugnon, Leandro A. es
sedici.creator.person Yones, Cristian es
sedici.creator.person Milone, Diego H. es
sedici.creator.person Stegmayer, Georgina es
sedici.description.note Extended abstract from Deep neural architectures for highly imbalanced data in bioinformatics, L. A. Bugnon, C. Yones, D. H. Milone, G. Stegmayer, (to appear in) IEEE Transactions on Neural Networks and Learning Systems (2019), doi 10.1109/TNNLS.2019.2914471 es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Sociedad Argentina de Informática e Investigación Operativa es
sedici.subtype Resumen es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/
sedici.date.exposure 2019-09
sedici.relation.event V Simposio Argentino de Ciencia de Datos y GRANdes DAtos (AGRANDA 2019) - JAIIO 48 (Salta) es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)