Exploració per autor "Romero Merino, Enrique"
Ara es mostren els items 9-28 de 52
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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 ... -
Extended linear models with Gaussian prior on the parameters and adaptive expansion vectors
(Springer, 2007)
Text en actes de congrés
Accés obertWe 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 Selection with Single-Layer Perceptrons for a multicentre 1H-MRS brain tumour database
(2009-06-12)
Article
Accés restringit per política de l'editorialA 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
(2000-02)
Report de recerca
Accés obertA 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 ... -
Function aproximation with SAOCIF: a general sequential method and a particular algorithm with feed-forward neural networks
(2001-10)
Report de recerca
Accés obertA sequential method for approximating vectors in Hilbert spaces, called Sequential Approximation with Optimal Coefficients and Interacting Frequencies (SAOCIF), is presented. SAOCIF combines two key ideas. The first one ... -
Identifying useful human correction feedback from an on-line machine translation service
(2013)
Text en actes de congrés
Accés obertPost-editing feedback provided by users of on-line translation services offers an excellent opportunity for automatic improvement of statistical machine translation (SMT) systems. However, feedback provided by casual users ... -
Identifying useful human feedback from an on-line translation service
(2013)
Comunicació de congrés
Accés obertPost-editing feedback provided by users of on-line translation services offers an excellent opportunity for automatic improvement of statistical machine translation (SMT) systems. However, feedback provided by casual ... -
Interpreting response to TMZ therapy in murine GL261 glioblastoma by combining Radiomics, Convex-NMF and feature selection in MRI/MRSI data analysis
(Institute of Electrical and Electronics Engineers (IEEE), 2020)
Text en actes de congrés
Accés obertMachine learning (ML) methods have shown great potential for the analysis of data involved in medical decisions. However, for these methods to be incorpored in the medical pipeline, they must be made interpretable not only ... -
Learning with Feed-forward Neural Networks: Three Schemes to Deal with the Bias/Variance Trade-off
(Universitat Politècnica de Catalunya, 2004-11-30)
Tesi
Accés obertIn terms of the Bias/Variance decomposition, very flexible (i.e., complex) Supervised Machine Learning systems may lead to unbiased estimators but with high variance. A rigid model, in contrast, may lead to small variance ...
