Exploració per autor "Cruz Barbosa, Raúl"
Ara es mostren els items 6-10 de 10
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On the computation of the geodesic distance with an application to dimensionality reduction in a neuro-oncology problem
Cruz Barbosa, Raúl; Bautista Villavicencio, David; Vellido Alcacena, Alfredo (Springer, 2011)
Text en actes de congrés
Accés restringit per política de l'editorialManifold learning models attempt to parsimoniously describe multivariate data through a low-dimensional manifold embedded in data space. Similarities between points along this manifold are often expressed as Euclidean ... -
On the improvement of the mapping trustworthiness and continuity of a manifold learning model
Cruz Barbosa, Raúl; Vellido Alcacena, Alfredo (2008-11)
Article
Accés restringit per política de l'editorialManifold learningmethodsmodel high-dimensional data through low-dimensional manifolds embedded in the observed data space. This simplification implies that their are prone to trustworthiness and continuity errors. Generative ... -
SVM-based classification of class C GPCRs from alignment-free physicochemical transformations of their sequences
König, Caroline; Cruz Barbosa, Raúl; Alquézar Mancho, René; Vellido Alcacena, Alfredo (Springer Berlin Heidelberg, 2013)
Text en actes de congrés
Accés obertG protein-coupled receptors (GPCRs) have a key function in regulating the function of cells due to their ability to transmit extracelullar signals. Given that the 3D structure and the functionality of most GPCRs is unknown, ... -
The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors
Cruz Barbosa, Raúl; Vellido Alcacena, Alfredo; Giraldo Arjonilla, Jesús (2015-02-01)
Article
Accés obert -
Unfolding the Manifold in Generative Topographic Mapping
Cruz Barbosa, Raúl; Vellido Alcacena, Alfredo (2008-09)
Article
Accés restringit per política de l'editorialGenerative Topographic Mapping (GTM) is a probabilistic latent variable model for multivariate data clustering and visualization. It tries to capture the relevant data structure by defining a low-dimensional manifold ...