Exploració per autor "Cruz Barbosa, Raúl"
Ara es mostren els items 1-10 de 10
-
Advances in semi-supervised alignment-free classification of G protein-coupled receptors
Cruz Barbosa, Raúl; Vellido Alcacena, Alfredo; Giraldo, Jesús (2013)
Text en actes de congrés
Accés obertG Protein-coupled receptors (GPCRs) are integral cell membrane proteins of great relevance for pharmacology due to their role in transducing extracellular signals. The 3-D s tructure is unknown for most of them, and the ... -
Comparative diagnostic accuracy of linear and nonlinear feature extraction methods 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'editorialThe diagnostic classification of human brain tumours on the basis of magnetic resonance spectra is a non-trivial problem in which dimensionality reduction is almost mandatory. This may take the form of feature selection ... -
Comparative evaluation of semi-supervised geodesic GTM
Cruz Barbosa, Raúl; Vellido Alcacena, Alfredo (2009)
Text en actes de congrés
Accés restringit per política de l'editorialIn many real problems that ultimately require data classification, not all the class labels are readily available. This concerns the field of semi-supervised learning, in which missing class labels must be inferred from ... -
Generative manifold learning for the exploration of partially labeled data
Cruz Barbosa, Raúl (Universitat Politècnica de Catalunya, 2009-10-01)
Tesi
Accés obertIn many real-world application problems, the availability of data labels for supervised learning is rather limited. Incompletely labeled datasets are common in many of the databases generated in some of the currently most ... -
Geodesic Generative Topographic Mapping
Cruz Barbosa, Raúl; Vellido Alcacena, Alfredo (2008-10)
Article
Accés restringit per política de l'editorialNonlinear dimensionality reduction (NLDR) methods aim to provide a faithful low-dimensional representation of multivariate data. The manifold learning family of NLDR methods, in particular, do this by defining low-dimensional ... -
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 ...