Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Vanessa Gómez-Verdejo"'
Autor:
Albert Belenguer-Llorens, Carlos Sevilla-Salcedo, Manuel Desco, Maria Luisa Soto-Montenegro, Vanessa Gómez-Verdejo
Publikováno v:
Applied Sciences, Vol 12, Iss 5, p 2571 (2022)
In this paper, we propose a novel Machine Learning Model based on Bayesian Linear Regression intended to deal with the low sample-to-variable ratio typically found in neuroimaging studies and focusing on mental disorders. The proposed model combines
Externí odkaz:
https://doaj.org/article/2e6fa549c9f34021814b722b0cc2d913
Autor:
Alejandro Guerrero-López, Carlos Sevilla-Salcedo, Ana Candela, Marta Hernández-García, Emilia Cercenado, Pablo M. Olmos, Rafael Cantón, Patricia Muñoz, Vanessa Gómez-Verdejo, Rosa del Campo, Belén Rodríguez-Sánchez
Publikováno v:
Engineering Applications of Artificial Intelligence. 118:105644
Publikováno v:
Computer methods and programs in biomedicine. 226
Machine learning techniques typically used in dementia assessment are not able to learn multiple tasks jointly and deal with time-dependent heterogeneous data containing missing values. In this paper, we reformulate SSHIBA, a recently introduced Baye
Autor:
Ana Candela, Vanessa Gómez-Verdejo, Marta Hernández-García, Rafael Cantón, Patricia Muñoz, Pablo M. Olmos, Emilia Cercenado, Carlos Sevilla-Salcedo, Rosa del Campo, Belén Rodríguez-Sánchez, Alejandro Guerrero-López
Matrix-Assisted Laser Desorption Ionization Time-Of-Flight (MALDI-TOF) Mass Spectrometry (MS) is a reference method for microbial identification and it can be used to predict Antibiotic Resistance (AR) when combined with artificial intelligence metho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a9be88c53c932c1d415d4d886a99c4c4
https://doi.org/10.1101/2021.10.04.463058
https://doi.org/10.1101/2021.10.04.463058
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname
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Multivariate Analysis (MVA) comprises a family of well-known methods for feature extraction which exploit correlations among input variables representing the data. One important property that is enjoyed by most such methods is uncorrelation among the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f98d6e7535757b9fbd3c9c7a0398d26f
Multitask Gaussian processes (MTGP) are the Gaussian process (GP) framework's solution for multioutput regression problems in which the $T$ elements of the regressors cannot be considered conditionally independent given the observations. Standard MTG
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::34ed83be8aa82c1231f46d963caa0eb7
http://arxiv.org/abs/2006.03495
http://arxiv.org/abs/2006.03495
Publikováno v:
Pattern Recognition
The Bayesian approach to feature extraction, known as factor analysis (FA), has been widely studied in machine learning to obtain a latent representation of the data. An adequate selection of the probabilities and priors of these bayesian models allo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d53e40aacc20917359d8eb7b809b664b
http://arxiv.org/abs/2001.08975
http://arxiv.org/abs/2001.08975
Autor:
Sancho Salcedo-Sanz, Manel Martínez-Ramón, M.Á. Hombrados-Herrera, C. Casanova-Mateo, J. Sanz-Justo, Silvia Jiménez-Fernández, Vanessa Gómez-Verdejo, Guillermo Terrén-Serrano, O. Garcia-Hinde
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname
instname
The interest in solar radiation prediction has increased greatly in recent times among the scientific community. In this context, Machine Learning techniques have shown their ability to learn accurate prediction models. The aim of this paper is to go
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname
instname
This paper proposes a framework in which a multivariate analysis method (MVA) guides a selection of input variables that leads to a sparse feature extraction. This framework, called parsimonious MVA, is specially suited for high dimensional data such
Autor:
Carlos Sevilla-Salcedo, Alzheimer’s Disease Neuroimaging Initiative, Jussi Tohka, Vanessa Gómez-Verdejo
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname
instname
Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a Group/Institutional Author. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the inve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a101e5d069aa5c6d59006588e1d13563
https://doi.org/10.1101/698134
https://doi.org/10.1101/698134