• A decision making support tool: The resilience management fuzzy controller 

    González Cardenas, Rubén; Nebot Castells, M. Àngela; Múgica Álvarez, Francisco; Vellido Alcacena, Alfredo (Institute of Electrical and Electronics Engineers (IEEE), 2017)
    Texto en actas de congreso
    Acceso abierto
    In this paper a fuzzy controller capable to perform an automated estimation of the period of time necessary to recover a resilience level is proposed. Estimations where made by considering realistic time-dependent action ...
  • Advances in clustering and visualization of time series using GTM through time 

    Olier Caparroso, Iván; Vellido Alcacena, Alfredo (2008-09)
    Artículo
    Acceso restringido por política de la editorial
    Most of the existing research on multivariate time series concerns supervised forecasting problems. In comparison, little research has been devoted to their exploration through unsupervised clustering and visualization. ...
  • Advances in machine learning and computational intelligence 

    Schleif, Frank-Michael; Biehl, Michael; Vellido Alcacena, Alfredo (2009-03)
    Artículo
    Acceso restringido por política de la editorial
  • Advances in semi-supervised alignment-free classification of G protein-coupled receptors 

    Cruz Barbosa, Raúl; Vellido Alcacena, Alfredo; Giraldo, Jesús (2013)
    Texto en actas de congreso
    Acceso abierto
    G 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 ...
  • A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases 

    Mocioiu, Victor; de Barros, Nuno M. Pedrosa; Ortega Martorell, Sandra; Slotboom, Johannes; Knecht, Urspeter; Arús, Carles; Vellido Alcacena, Alfredo; Julià Sapé, Margarida (I6doc.com, 2016)
    Texto en actas de congreso
    Acceso abierto
    Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to ...
  • A MAP approach for convex non-negative matrix factorization in the diagnosis of brain tumors 

    Vilamala Muñoz, Albert; Belanche Muñoz, Luis Antonio; Vellido Alcacena, Alfredo (2014)
    Texto en actas de congreso
    Acceso restringido por política de la editorial
    Convex non-negative matrix factorization is a blind signal separation technique that has previously demonstrated to be well-suited for the task of human brain tumor diagnosis from magnetic resonance spectroscopy data. This ...
  • A methodological approach for algorithmic composition systems' parameter spaces aesthetic exploration 

    Paz Ortiz, Iván; Nebot Castells, M. Àngela; Romero Merino, Enrique; Múgica Álvarez, Francisco; Vellido Alcacena, Alfredo (Institute of Electrical and Electronics Engineers (IEEE), 2017)
    Comunicación de congreso
    Acceso abierto
    Algorithmic composition is the process of creating musical material by means of formal methods. As a consequence of its design, algorithmic composition systems are (explicitly or implicitly) described in terms of parameters. ...
  • A probabilistic approach to the visual exploration of G protein-coupled receptor sequences 

    Vellido Alcacena, Alfredo; Cárdenas, Martha Ivón; Olier Caparroso, Iván; Rovira, Xavier; Giraldo, Jesús (2011)
    Texto en actas de congreso
    Acceso abierto
    The study of G protein-coupled receptors (GPCRs) is of great interest in pharmaceutical research, but only a few of their 3D structures are known at present. On the contrary, their amino acid sequences are known and ...
  • A Quotient Basis Kernel for the prediction of mortality in severe sepsis patients 

    Ribas Ripoll, Vicent; Romero Merino, Enrique; Ruiz Rodríguez, Juan Carlos; Vellido Alcacena, Alfredo (2013)
    Texto en actas de congreso
    Acceso abierto
    In this paper, we describe a novel kernel for multinomial distributions, namely the Quotient Basis Kernel (QBK), which is based on a suitable reparametrization of the input space through algebraic geometry and statistics. ...
  • Automated classification of brain tumours from short echo time in vivo MRS data using Gaussian decomposition and Bayesian neural networks 

    Arizmendi Pereira, Carlos Julio; Sierra Bueno, Daniel Alfonso; Vellido Alcacena, Alfredo; Romero Merino, Enrique (2014-09)
    Artículo
    Acceso restringido por política de la editorial
    Neuro-oncologists must ultimately rely on their acquired knowledge and accumulated experience to undertake the sensitive task of brain tumour diagnosis. This task strongly depends on indirect, non-invasive measurements, ...
  • Automated quality control for proton magnetic resonance spectroscopy data using convex non-negative matrix factorization 

    Mocioiu, Victor; Kyathanahally, Sreenath P.; Arús, Carles; Vellido Alcacena, Alfredo; Julià Sapé, Margarida (Springer, 2016)
    Texto en actas de congreso
    Acceso abierto
    Proton Magnetic Resonance Spectroscopy (1H MRS) has proven its diagnostic potential in a variety of conditions. However, MRS is not yet widely used in clinical routine because of the lack of experts on its diagnostic ...
  • A variational Bayesian formulation for GTM: Theoretical foundations 

    Olier Caparroso, Iván; Vellido Alcacena, Alfredo (2007-09)
    Report de recerca
    Acceso abierto
    Generative Topographic Mapping (GTM) is a non-linear latent variable model of the manifold learning family that provides simultaneous visualization and clustering of high-dimensional data. It was originally formulated as ...
  • A variational formulation for GTM through time 

    Olier Caparroso, Iván; Vellido Alcacena, Alfredo (IEEE, 2008)
    Texto en actas de congreso
    Acceso abierto
    Generative Topographic Mapping (GTM) is a latent variable model that, in its original version, was conceived to provide clustering and visualization of multivariate, realvalued, i.i.d. data. It was also extended to deal ...
  • A variational formulation for GTM through time: Theoretical foundations 

    Olier Caparroso, Iván; Vellido Alcacena, Alfredo (2007-10)
    Report de recerca
    Acceso abierto
    Generative Topographic Mapping (GTM) is a latent variable model that, in its standard version, was conceived to provide clustering and visualization of multivariate, real-valued, i.i.d. data. It was also extended to deal ...
  • A weighted Cramér’s V Index for the assessment of stability in the fuzzy clustering of class C G protein-coupled receptors 

    Vellido Alcacena, Alfredo; Halka, Christiana; Nebot Castells, M. Àngela (Springer, 2015)
    Texto en actas de congreso
    Acceso abierto
    After decades of intensive use, K-Means is still a common choice for crisp data clustering in real-world applications, particularly in biomedicine and bioinformatics. It is well-known that different initializations of the ...
  • A weighted Cramer's V index for the assessment of stability in the fuzzy clustering of class C G protein-coupled receptors 

    Vellido Alcacena, Alfredo; Halka, Christiana; Nebot Castells, M. Àngela (Springer, 2015)
    Capítulo de libro
    Acceso abierto
    After decades of intensive use, K-Means is still a common choice for crisp data clustering in real-world applications, particularly in biomedicine and bioinformatics. It is well-known that different initializations of the ...
  • Bayesian semi non-negative matrix factorisation 

    Vilamala Muñoz, Albert; Vellido Alcacena, Alfredo; Belanche Muñoz, Luis Antonio (I6doc.com, 2016)
    Texto en actas de congreso
    Acceso abierto
    Non-negative Matrix Factorisation (NMF) has become a standard method for source identification when data, sources and mixing coefficients are constrained to be positive-valued. The method has recently been extended to allow ...
  • Capturing the dynamics of multivariate time series through visualization using generative topographic mapping through time 

    Olier, Ivan; Vellido Alcacena, Alfredo (2005-11)
    Report de recerca
    Acceso abierto
    Most of the existing research on time series concerns supervised forecasting problems. In comparison, little research has been devoted to unsupervised methods for the visual exploration of multivariate time series. In this ...
  • Cartogram representation of the batch-SOM magnification factor 

    Tosi, Alessandra; Vellido Alcacena, Alfredo (2012)
    Texto en actas de congreso
    Acceso restringido por política de la editorial
    Model interpretability is a problem of knowledge extraction from the patterns found in raw data. One key source of knowledge is information visualization, which can help us to gain insights into a problem through graphical ...
  • Classification, dimensionality reduction, and maximally discriminatory visualization of a multicentre 1H-MRS database of brain tumors 

    Lisboa, Paulo J.G.; Romero Merino, Enrique; Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Arús, Carles (IEEE, 2008)
    Texto en actas de congreso
    Acceso abierto
    The combination of an Artificial Neural Network classifier, a feature selection process, and a novel linear dimensionality reduction technique that provides a data projection for visualization and which preserves completely ...