Now showing items 21-40 of 79

    • Distance-based kernels for real-valued data 

      Belanche Muñoz, Luis Antonio; Vázquez Suárez, Juan Luis; Vázquez García, Miguel (Springer, 2008)
      Conference report
      Open Access
      We consider distance-based similarity measures for real-valued vectors of interest in kernel-based machine learning algorithms. In particular, a truncated Euclidean similarity measure and a self-normalized similarity measure ...
    • Effective classification and gene expression profiling for the facioscapulohumeral muscular dystrophy 

      González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio; Silva Colón, Karen Andrea (2013-12-13)
      Article
      Open Access
      The Facioscapulohumeral Muscular Dystrophy (FSHD) is an autosomal dominant neuromuscular disorder whose incidence is estimated in about one in 400,000 to one in 20,000. No effective therapeutic strategies are known to halt ...
    • Evolutionary optimization of heterogeneous problems 

      Belanche Muñoz, Luis Antonio (Springer, 2002)
      Conference report
      Open Access
      A large number of practical optimization problems involve elements of quite diverse nature described as mixtures of qualitative and quantitative information and whose description is possibly incomplete. In this work we ...
    • Evolutionary optimization of heterogeneous problems 

      Belanche Muñoz, Luis Antonio (2003-02)
      External research report
      Open Access
      A large number of practical optimization problems involve elements of quite diverse nature, described as mixtures of qualitative and quantitative information, and whose description is possibly incomplete. In this work we ...
    • Exploiting the accumulated evidence for gene selection in microarray gene expression data 

      Prat Masramon, Gabriel; Belanche Muñoz, Luis Antonio (IOS Press, 2010)
      Conference report
      Open Access
      Feature subset selection (FSS) methods play an important role for cancer classification using microarray gene expression data. In this scenario, it is extremely important to select genes by taking into account the possible ...
    • Exploiting the accumulated evidence for gene selection in microarray gene expression data 

      Prat Masramon, Gabriel; Belanche Muñoz, Luis Antonio (2013)
      External research report
      Open Access
      Machine Learning methods have of late made signicant efforts to solving multidisciplinary problems in the field of cancer classification using microarray gene expression data. Feature subset selection methods can play an ...
    • Extended linear models with Gaussian prior on the parameters and adaptive expansion vectors 

      Barrio Moliner, Ignacio; Romero Merino, Enrique; Belanche Muñoz, Luis Antonio (Springer, 2007)
      Conference report
      Open Access
      We present an approximate Bayesian method for regression and classification with models linear in the parameters. Similar to the Relevance Vector Machine (RVM), each parameter is associated with an expansion vector. Unlike ...
    • Feature and model selection in 1H-MRS single voxel spectra for cancer classification 

      González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio (Future Technology Press, 2009)
      Part of book or chapter of book
      Restricted access - publisher's policy
      Machine learning is a powerful paradigm within which to analyze 1HMRS spectral data for the classification of tumour pathologies. An important characteristic of this task is the high dimensionality of the involved data ...
    • Feature selection algorithms: a survey and experimental evaluation 

      Molina Félix, Luis Carlos; Belanche Muñoz, Luis Antonio; Nebot Castells, M. Àngela (Institute of Electrical and Electronics Engineers (IEEE), 2002)
      Conference report
      Open Access
      In view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enables to adequately decide which algorithm to use in certain situations. This work assesses the ...
    • Feature selection algorithms: a survey and experimental evaluation 

      Molina, Luis; Belanche Muñoz, Luis Antonio; Nebot Castells, M. Àngela (2003-02)
      External research report
      Open Access
      In view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enables to adequately decide which algorithm to use in certain situations. This work reviews several ...
    • Feature selection for microarray gene expression data using simulated annealing guided by the multivariate joint entropy 

      González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio (2014-04-01)
      Article
      Open Access
      Microarray classification poses many challenges for data analysis, given that a gene expression data set may consist of dozens of observations with thousands or even tens of thousands of genes. In this context, feature ...
    • Feature selection for microarray gene expression data using simulated annealing guided by the multivariate joint entropy 

      González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio (2013)
      External research report
      Open Access
      In this work a new way to calculate the multivariate joint entropy is presented. This measure is the basis for a fast information-theoretic based evaluation of gene relevance in a Microarray Gene Expression data context. ...
    • Feature selection for the prediction and visualization of brain tumor types using proton magnetic resonance spectroscopy data 

      González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio (Springer, 2012)
      Part of book or chapter of book
      Restricted access - publisher's policy
      In cancer diagnosis, classification of the different tumor types is of great importance. An accurate prediction of basic tumor types provides better treatment and may minimize the negative impact of incorrectly targeted ...
    • Feature selection in proton magnetic resonance spectroscopy data of brain tumors 

      González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio (Università degli Studi di Salerno, 2011)
      Conference report
      Open Access
      In cancer diagnosis, classification of the different tumor types is of great importance. An accurate prediction of different tumor types provides better treatment and may minimize the negative impact of incorrectly targeted ...
    • Feature selection in proton magnetic resonance spectroscopy for brain tumor classification 

      González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio (2008)
      Conference report
      Open Access
      H-MRS is a technique that uses response of protons under certain magnetic conditions to reveal the biochemical structure of human tissue. An important application is found in brain tumor diagnosis, due to the known ...
    • Forecasting financial time series with multiple kernel learning 

      Fábregues de los Santos, Luis; Arratia Quesada, Argimiro Alejandro; Belanche Muñoz, Luis Antonio (2017)
      Conference lecture
      Restricted access - publisher's policy
      This paper introduces a forecasting procedure based on mul-tivariate dynamic kernels to re-examine –under a non linear framework–the experimental tests reported by Welch and Goyal showing that severalvariables proposed in ...
    • Fuzzy heterogeneous heurons for imprecise classification problems 

      Valdés Ramos, Julio José; Belanche Muñoz, Luis Antonio; Alquézar Mancho, René (1998-06)
      External research report
      Open Access
      In the classical neuron model, inputs are continuous real-valued quantities. However, in many important domains from the real world, objects are described by a mixture of continuous and discrete variables, usually containing ...
    • Fuzzy heterogeneous neural networks for signal forecasting 

      Belanche Muñoz, Luis Antonio; Valdés Ramos, Julio José; Alquézar Mancho, René (Springer, 1998)
      Conference report
      Open Access
      Fuzzy heterogeneous neural networks are recently introduced models based on neurons accepting heterogeneous inputs (i.e. mixtures of numerical and non-numerical information possibly with missing data) with either crisp or ...
    • Fuzzy heterogeneous neurons for imprecise classification problems 

      Valdés Ramos, Julio José; Belanche Muñoz, Luis Antonio; Alquézar Mancho, René (Wiley, 2000-02)
      Article
      Open Access
      In the classical neuron model, inputs are continuous real-valued quantities. However, in many important domains from the real world, objects are described by a mixture of continuous and discrete variables, usually containing ...
    • Fuzzy inputs and missing data in similarity-based heterogeneous neural networks 

      Belanche Muñoz, Luis Antonio; Valdés Ramos, Julio José (Springer, 1999)
      Conference report
      Open Access
      Fuzzy heterogeneous networks are recently introduced neural network models composed of neurons of a general class whose inputs and weights are mixtures of continuous variables (crisp and/or fuzzy) with discrete quantities, ...