Mostrar el registro sencillo del ítem
dc.contributor.author
Caiafa, César Federico
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.contributor.author
Zhe, Sun
dc.contributor.author
Tanaka, Toshihisa
dc.contributor.author
Marti Puig, Pere
dc.contributor.author
Solé Casals, Jordi
dc.date.available
2021-07-01T15:26:40Z
dc.date.issued
2021-04
dc.identifier.citation
Caiafa, César Federico; Zhe, Sun ; Tanaka, Toshihisa ; Marti Puig, Pere; Solé Casals, Jordi; Machine Learning Methods with Noisy, Incomplete or Small Datasets; MDPI; Applied Sciences; 11; 9; 4-2021; 1-4
dc.identifier.issn
2076-3417
dc.identifier.uri
http://hdl.handle.net/11336/135279
dc.description.abstract
In this article, we present a collection of fifteen novel contributions on machine learning methods with low-quality or imperfect datasets, which were accepted for publication in the special issue “Machine Learning Methods with Noisy, Incomplete or Small Datasets”, Applied Sciences (ISSN 2076-3417). These papers provide a variety of novel approaches to real-world machine learning problems where available datasets suffer from imperfections such as missing values, noise or artefacts. Contributions in applied sciences include medical applications, epidemic management tools, methodological work, and industrial applications, among others. We believe that this special issue will bring new ideas for solving this challenging problem, and will provide clear examples of application in real-world scenarios.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
MDPI
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Machine learning
dc.subject
artificial intelligence
dc.subject
neural networks
dc.subject.classification
Otras Ciencias de la Computación e Información
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.subject.classification
Ciencias de la Computación e Información
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.title
Machine Learning Methods with Noisy, Incomplete or Small Datasets
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-06-10T19:28:06Z
dc.journal.volume
11
dc.journal.number
9
dc.journal.pagination
1-4
dc.journal.pais
Suiza
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.journal.ciudad
Basel
dc.description.fil
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
dc.description.fil
Fil: Zhe, Sun. Lab. Adaptive Intelligence - Riken; Japón
dc.description.fil
Fil: Tanaka, Toshihisa. Tokyo University of Agriculture and Technology; Japón
dc.description.fil
Fil: Marti Puig, Pere. University of Vic; España
dc.description.fil
Fil: Solé Casals, Jordi. University of Vic; España
dc.journal.title
Applied Sciences
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2076-3417/11/9/4132
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3390/app11094132
Archivos asociados