Exploració per autor "Romero Merino, Enrique"
Ara es mostren els items 1-20 de 52
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A fuzzy inductive approach for rule-based modelling of high level structures in algorithmic composition systems
(2015)
Text en actes de congrés
Accés restringit per política de l'editorialAlgorithmic 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
(Springer, 2017)
Capítol de llibre
Accés obertAlgorithmic 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
(Institute of Electrical and Electronics Engineers (IEEE), 2017)
Comunicació de congrés
Accés obertAlgorithmic 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
(2013)
Text en actes de congrés
Accés obertIn 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
(2005-10)
Report de recerca
Accés obertAn 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 ... -
Ad-RuLer: A novel rule-driven data synthesis technique for imbalanced classification
(Multidisciplinary Digital Publishing Institute, 2023-11-23)
Article
Accés obertWhen classifiers face imbalanced class distributions, they often misclassify minority class samples, consequently diminishing the predictive performance of machine learning models. Existing oversampling techniques predominantly ... -
Assessment of electrocardiograms with pretraining and shallow networks
(Computing in Cardiology, 2014)
Text en actes de congrés
Accés obertObjective: 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
(2014-09)
Article
Accés restringit per política de l'editorialNeuro-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
(Institute of Electrical and Electronics Engineers (IEEE), 2017)
Text en actes de congrés
Accés obertRecent 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
(Institute of Electrical and Electronics Engineers (IEEE), 2019)
Comunicació de congrés
Accés obertAlgorithmic 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
(IEEE, 2008)
Text en actes de congrés
Accés obertThe 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
(IOS Press, 2018)
Capítol de llibre
Accés obertOperating 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
(2012-01)
Article
Accés restringit per política de l'editorialRecently, 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
(2010-06)
Report de recerca
Accés obertRecently, 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
(2009)
Text en actes de congrés
Accés obertThe 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
(2009)
Text en actes de congrés
Accés obertA 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
(2016-12-01)
Article
Accés restringit per política de l'editorialIn 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
(2003-12)
Report de recerca
Accés obertAn 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
(Future Technology Press, 2009-01-31)
Capítol de llibre
Accés restringit per política de l'editorialNon-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
(2008-09)
Article
Accés restringit per política de l'editorialAs 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 ...
