Ara es mostren els items 1-20 de 36

  • A fuzzy inductive approach for rule-based modelling of high level structures in algorithmic composition systems 

    Múgica Álvarez, Francisco; Paz Ortiz, Iván; Nebot Castells, M. Àngela; Romero Merino, Enrique (2015)
    Text en actes de congrés
    Accés restringit per política de l'editorial
    Algorithmic composition systems are now widely understood. However, its capacity for producing outputs consistently showing high level structures is still a field of research. In the present work, the Fuzzy Inductive ...
  • A fuzzy rule model for high level musical features on automated composition systems 

    Paz Ortiz, Iván; Nebot Castells, M. Àngela; Múgica Álvarez, Francisco; Romero Merino, Enrique (Springer, 2017)
    Capítol de llibre
    Accés obert
    Algorithmic composition systems are now well-understood. However, when they are used for specific tasks like creating material for a part of a piece, it is common to prefer, from all of its possible outputs, those exhibiting ...
  • 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)
    Comunicació de congrés
    Accés obert
    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 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)
    Text en actes de congrés
    Accés obert
    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. ...
  • A sequential algorithm for feed-forward neural networks with optimal coefficients and interacting frequencies 

    Romero Merino, Enrique; Alquézar Mancho, René (2005-10)
    Report de recerca
    Accés obert
    An algorithm for sequential approximation with optimal coefficients and interacting frequencies (SAOCIF) for feed-forward neural networks is presented. SAOCIF combines two key ideas. The first one is the optimization of ...
  • Assessment of electrocardiograms with pretraining and shallow networks 

    Ribas Ripoll, Vicent; Wojdel, Anna; Ramos, Pablo; Romero Merino, Enrique; Brugada Terradellas, Josep (Computing in Cardiology, 2014)
    Text en actes de congrés
    Accés obert
    Objective: Clinical Decision Support Systems normally resort to annotated signals for the automatic assessment of ECG signals. In this paper we put forward a new method for the assessment of normal/abnormal heart function ...
  • 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)
    Article
    Accés restringit per política de l'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, ...
  • Benchmarking the selection of the hidden-layer weights in extreme learning machines 

    Romero Merino, Enrique (Institute of Electrical and Electronics Engineers (IEEE), 2017)
    Text en actes de congrés
    Accés obert
    Recent years have seen a growing interest in neural networks whose hidden-layer weights are randomly selected, such as Extreme Learning Machines (ELMs). These models are motivated by their ease of development, high ...
  • 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)
    Text en actes de congrés
    Accés obert
    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 ...
  • Classifying and generalizing successful parameter combinations for sound design 

    Paz, Iván; Nebot Castells, M. Àngela; Múgica Álvarez, Francisco; Romero Merino, Enrique (IOS Press, 2018)
    Capítol de llibre
    Accés obert
    Operating parametric systems in the context of sound design imposes cognitive and practical challenges. The present contribution applies rule extraction to analyze and to generalize a set of parameter combinations, which ...
  • Comparing error minimized extreme learning machines and support vector sequential feed-forward neural networks 

    Romero Merino, Enrique; Alquézar Mancho, René (2012-01)
    Article
    Accés restringit per política de l'editorial
    Recently, error minimized extreme learning machines (EM-ELMs) have been proposed as a simple and efficient approach to build single-hidden-layer feed-forward networks (SLFNs) sequentially. They add random hidden nodes one ...
  • Comparing error minimized extreme learning machines and support vector sequential feed-forward neural networks 

    Romero Merino, Enrique; Alquézar Mancho, René (2010-06)
    Report de recerca
    Accés obert
    Recently, error minimized extreme learning machines (EM-ELMs) have been proposed as a simple and efficient approach to build single-hidden-layer feed-forward networks (SLFNs) sequentially. They add random hidden nodes one ...
  • Data mining of patients on weaning trials from mechanical ventilation using cluster analysis and neural networks 

    Arizmendi Pereira, Carlos Julio; Romero Merino, Enrique; Alquézar Mancho, René; Caminal Magrans, Pere; Díaz, Ivan; Benito, Salvador; Giraldo Giraldo, Beatriz (2009)
    Text en actes de congrés
    Accés obert
    The process of weaning from mechanical ventilation is one of the challenges in intensive care. 149 patients under extubation process (T-tube test) were studied: 88 patients with successful trials (group S), 38 patients ...
  • Discriminating glioblastomas from metastases in a SV1H-MRS brain tumour database 

    Romero Merino, Enrique; Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Arús, Carles (2009)
    Text en actes de congrés
    Accés obert
    A Feature Selection (FS) process with a simple Machine Learning method, namely the Single-Layer Perceptron (SLP), is shown to discriminate metastases from glioblastomas with high accuracy using single voxel H-MRS from an ...
  • ECG assessment based on neural networks with pretraining 

    Ribas Ripoll, Vicent; Wojdel, Anna; Romero Merino, Enrique; Ramos, Pablo; Brugada Terradellas, Josep (2016-12-01)
    Article
    Accés restringit per política de l'editorial
    In this paper, we present a new automatic screening method to assess whether a patient from ambulatory care or emergency should be referred to a cardiology service. This method is based on deep neural networks with pretraining ...
  • Exploiting diversity of margin-based classifiers 

    Romero Merino, Enrique; Carreras Pérez, Xavier; Màrquez Villodre, Lluís (2003-12)
    Report de recerca
    Accés obert
    An experimental comparison among Support Vector Machines, AdaBoost and a recently proposed model for maximizing the margin with Feed-forward Neural Networks has been made on a real-world classification problem, namely ...
  • Exploratory characterization of a multi-centre 1H-MRS brain tumour database 

    Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Romero Merino, Enrique; Arús, Carles (Future Technology Press, 2009-01-31)
    Capítol de llibre
    Accés restringit per política de l'editorial
    Non-invasive techniques such asMagnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) are often required for the diagnosis of tumours for which conclusive biopsies are not commonly available.While ...
  • Exploratory characterization of outliers in a multi-centre 1H-MRS brain tumour dataset 

    Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Romero Merino, Enrique; Arús, Carles (2008-09)
    Article
    Accés restringit per política de l'editorial
    As part of the AIDTumour research project, the analysis of MRS data corresponding to various tumour pathologies is used to assist expert diagnosis. The high dimensionality of the MR spectra might obscure atypical aspects ...
  • Feature Selection with Single-Layer Perceptrons for a multicentre 1H-MRS brain tumour database 

    Romero Merino, Enrique; Vellido Alcacena, Alfredo; Sopena, Josep Maria (2009-06-12)
    Article
    Accés restringit per política de l'editorial
    A Feature Selection process with Single-Layer Perceptrons is shown to provide optimum discrimination of an international, multi-centre 1H-MRS database of brain tumors at reasonable computational cost. Results are both ...
  • Function approximation in Hilbert spaces: a general sequential method and a particular implementation with neural networks 

    Romero Merino, Enrique (2000-02)
    Report de recerca
    Accés obert
    A sequential method for approximating vectors in Hilbert spaces, called Sequential Approximation with Optimal Coefficients (SAOC), is presented. Most of the existing sequential methods choose the new term so that it ...