Machine Learning Methods with Noisy, Incomplete or Small Datasets
- Autores
- Caiafa, César Federico; Zhe, Sun; Tanaka, Toshihisa; Marti Puig, Pere; Solé Casals, Jordi
- Año de publicación
- 2021
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- 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.
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
Fil: Zhe, Sun. Lab. Adaptive Intelligence - Riken; Japón
Fil: Tanaka, Toshihisa. Tokyo University of Agriculture and Technology; Japón
Fil: Marti Puig, Pere. University of Vic; España
Fil: Solé Casals, Jordi. University of Vic; España - Materia
-
Machine learning
artificial intelligence
neural networks - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/135279
Ver los metadatos del registro completo
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Machine Learning Methods with Noisy, Incomplete or Small DatasetsCaiafa, César FedericoZhe, SunTanaka, ToshihisaMarti Puig, PereSolé Casals, JordiMachine learningartificial intelligenceneural networkshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In 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.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; ArgentinaFil: Zhe, Sun. Lab. Adaptive Intelligence - Riken; JapónFil: Tanaka, Toshihisa. Tokyo University of Agriculture and Technology; JapónFil: Marti Puig, Pere. University of Vic; EspañaFil: Solé Casals, Jordi. University of Vic; EspañaMDPI2021-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/135279Caiafa, 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; 1-42076-3417CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2076-3417/11/9/4132info:eu-repo/semantics/altIdentifier/doi/10.3390/app11094132info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:56:21Zoai:ri.conicet.gov.ar:11336/135279instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:56:21.439CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Machine Learning Methods with Noisy, Incomplete or Small Datasets |
title |
Machine Learning Methods with Noisy, Incomplete or Small Datasets |
spellingShingle |
Machine Learning Methods with Noisy, Incomplete or Small Datasets Caiafa, César Federico Machine learning artificial intelligence neural networks |
title_short |
Machine Learning Methods with Noisy, Incomplete or Small Datasets |
title_full |
Machine Learning Methods with Noisy, Incomplete or Small Datasets |
title_fullStr |
Machine Learning Methods with Noisy, Incomplete or Small Datasets |
title_full_unstemmed |
Machine Learning Methods with Noisy, Incomplete or Small Datasets |
title_sort |
Machine Learning Methods with Noisy, Incomplete or Small Datasets |
dc.creator.none.fl_str_mv |
Caiafa, César Federico Zhe, Sun Tanaka, Toshihisa Marti Puig, Pere Solé Casals, Jordi |
author |
Caiafa, César Federico |
author_facet |
Caiafa, César Federico Zhe, Sun Tanaka, Toshihisa Marti Puig, Pere Solé Casals, Jordi |
author_role |
author |
author2 |
Zhe, Sun Tanaka, Toshihisa Marti Puig, Pere Solé Casals, Jordi |
author2_role |
author author author author |
dc.subject.none.fl_str_mv |
Machine learning artificial intelligence neural networks |
topic |
Machine learning artificial intelligence neural networks |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
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. 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 Fil: Zhe, Sun. Lab. Adaptive Intelligence - Riken; Japón Fil: Tanaka, Toshihisa. Tokyo University of Agriculture and Technology; Japón Fil: Marti Puig, Pere. University of Vic; España Fil: Solé Casals, Jordi. University of Vic; España |
description |
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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-04 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/135279 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 2076-3417 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/135279 |
identifier_str_mv |
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 2076-3417 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2076-3417/11/9/4132 info:eu-repo/semantics/altIdentifier/doi/10.3390/app11094132 |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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openAccess |
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https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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MDPI |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.13397 |