Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Juan José Rodríguez Diez"'
Autor:
María Consuelo Sáiz Manzanares, Juan José Rodríguez Diez, María José Zaparaín Yáñez, Raúl Marticorena Sánchez, Rebeca Cerezo Menéndez
Publikováno v:
Sustainability, Vol 12, Iss 5, p 1970 (2020)
Scopus
Repositorio Institucional de la Universidad de Burgos (RIUBU)
instname
RUO: Repositorio Institucional de la Universidad de Oviedo
Universidad de Oviedo (UNIOVI)
RUO. Repositorio Institucional de la Universidad de Oviedo
Sustainability
Volume 12
Issue 5
Scopus
Repositorio Institucional de la Universidad de Burgos (RIUBU)
instname
RUO: Repositorio Institucional de la Universidad de Oviedo
Universidad de Oviedo (UNIOVI)
RUO. Repositorio Institucional de la Universidad de Oviedo
Sustainability
Volume 12
Issue 5
The use of learning environments that apply Advanced Learning Technologies (ALTs) and Self-Regulated Learning (SRL) is increasingly frequent. In this study, eye-tracking technology was used to analyze scan-path differences in a History of Art learnin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fbb6b25c8d0d8779e98ad36eb9dcedba
https://www.mdpi.com/2071-1050/12/5/1970
https://www.mdpi.com/2071-1050/12/5/1970
Autor:
Esteban R. Gelso, Oscar J. Prieto, Carlos Alonso González, Juan José Rodríguez Diez, Belarmino Pulido
Publikováno v:
IFAC Proceedings Volumes. 38:179-184
This paper describes an integrated approach to diagnosis of complex dynamic systems, combining model based diagnosis with machine learning techniques, proposing a simple framework to make them cooperate, hence improving the diagnosis capabilities of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::83ec086ed7f7ff8969e4c35d29f1c2c1
https://doi.org/10.3182/20050703-6-cz-1902.01832
https://doi.org/10.3182/20050703-6-cz-1902.01832
Autor:
Juan José Rodríguez Diez, Anibal Bregon, M. Aranzazu Simon, Carlos J. Alonso-González, Oscar J. Prieto, Isaac Moro, Belarmino Pulido
Publikováno v:
Scopus-Elsevier
Consistency-based diagnosis automatically provides fault detection and localization capabilities, using just models for correct behavior. However, it may exhibit a lack of discrimination power. Knowledge about fault modes can be added to tackle the p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d5644142860917d249af92b5605ec9eb
https://doi.org/10.23919/ecc.2007.7068686
https://doi.org/10.23919/ecc.2007.7068686
Publikováno v:
Pattern Recognition and String Matching ISBN: 9781461379522
This work presents a learning system for the classification of multivariate time series. This classification is useful in domains such as biomedical signals [9], continuous systems diagnosis [2] or data mining in temporal databases [3] .
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::25b60dee5f67502bc677b69f13decb21
https://doi.org/10.1007/978-1-4613-0231-5_6
https://doi.org/10.1007/978-1-4613-0231-5_6
Publikováno v:
Multiple Classifier Systems ISBN: 9783540422846
Multiple Classifier Systems
Multiple Classifier Systems
This work proposes a novel method for constructing RBF networks, based on boosting. The task assigned to the base learner is to select a RBF, while the boosting algorithm combines linearly the different RBFs. For each iteration of boosting a new neur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6ae2bbf523ea0a663fb31eca65dfa93a
https://doi.org/10.1007/3-540-48219-9_5
https://doi.org/10.1007/3-540-48219-9_5
Publikováno v:
INTELIGENCIA ARTIFICIAL. 4
Se presenta un metodo para la clasificacion de series temporales, incluyendo el caso multivariable. Se basa en la aplicacion de boosting sobre clasificadores muy simples: clausulas con solo un literal en el cuerpo. Los predicados utilizados estan bas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::05d3c185fc19bdf8e08b88483f80d895
https://doi.org/10.4114/ia.v4i11.686
https://doi.org/10.4114/ia.v4i11.686
Publikováno v:
Multiple Classifier Systems ISBN: 9783540677048
Multiple Classifier Systems
Multiple Classifier Systems
A supervised classification method for temporal series, even multivariate, is presented. It is based on boosting very simple classifiers, which consists only of one literal. The proposed predicates are based in similarity functions (i.e., euclidean a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a1e5a7b49703d2748c49307cf8bcc30
https://doi.org/10.1007/3-540-45014-9_20
https://doi.org/10.1007/3-540-45014-9_20