Now showing items 1-20 of 43

    • 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)
      Conference report
      Restricted access - publisher's policy
      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)
      Part of book or chapter of book
      Open Access
      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)
      Conference lecture
      Open Access
      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)
      Conference report
      Open Access
      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)
      External research report
      Open Access
      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)
      Conference report
      Open Access
      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
      Restricted access - publisher's policy
      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)
      Conference report
      Open Access
      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 ...
    • Charting perceptual spaces with fuzzy rules 

      Paz Ortiz, Alejandro Iván; Nebot Castells, M. Àngela; Romero Merino, Enrique; Múgica Álvarez, Francisco (Institute of Electrical and Electronics Engineers (IEEE), 2019)
      Conference lecture
      Open Access
      Algorithmic music nowadays performs domain specific tasks for which classical algorithms do not offer optimal solutions or require user's expertise. Among these tasks is the extraction of models from data that offer an ...
    • 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)
      Conference report
      Open Access
      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)
      Part of book or chapter of book
      Open Access
      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 (2010-06)
      External research report
      Open Access
      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 (2012-01)
      Article
      Restricted access - publisher's policy
      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)
      Conference report
      Open Access
      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)
      Conference report
      Open Access
      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
      Restricted access - publisher's policy
      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)
      External research report
      Open Access
      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)
      Part of book or chapter of book
      Restricted access - publisher's policy
      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
      Restricted access - publisher's policy
      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 ...
    • 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 ...