E. Romero

发表

Neural networks with periodic and monotonic activation functions: a comparative study in classification problems

J. Sopena, E. Romero, R. Alquézar, 1999 .

A new incremental method for function approximation using feed-forward neural networks

E. Romero, R. Alquézar, 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

Functions in Regression

E. Romero, Ignacio Barrio, L. Belanche, 2007 .

A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

Paulo J. G. Lisboa, Enrique Romero, Margarida Julià-Sapé, 2013, PloS one.

Data mining of patients on weaning trials from mechanical ventilation using cluster analysis and neural networks

Enrique Romero, Pere Caminal, Beatriz F. Giraldo, 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Unraveling response to temozolomide in preclinical GL261 glioblastoma with MRI/MRSI using radiomics and signal source extraction

A. Vellido, M. Julià-Sapé, C. Arús, 2020, Scientific Reports.

DataandKnowledge Visualization withVirtual Reality Spaces, NeuralNetworks andRoughSets: Application toGeophysical Prospecting

E. Romero, 2007 .

Multiparametric MR approaches for non-invasive Glioblastoma therapy response follow-up (MAGRes)

A. Vellido, M. Julià-Sapé, C. Arús, 2019 .

Using the Fuzzy Inductive Reasoning methodology to improve coherence in algorithmic musical beat patterns

Enrique Romero, Àngela Nebot, Francisco Mugica, 2014, CCIA.

Heuristics for the selection of weights in sequential feed-forward neural networks: An experimental study

Enrique Romero, René Alquézar, E. Romero, 2007, Neurocomputing.

Selection of Basis Functions Guided by the L2 Soft Margin

Lluís A. Belanche Muñoz, Enrique Romero, Ignacio Barrio, 2007, ICANN.

Extended Linear Models with Gaussian Prior on the Parameters and Adaptive Expansion Vectors

Lluís A. Belanche Muñoz, Enrique Romero, Ignacio Barrio, 2007, ICANN.

Sepsis mortality prediction with the Quotient Basis Kernel

Alfredo Vellido, Enrique Romero, Vicent J. Ribas, 2014, Artif. Intell. Medicine.

Margin maximization with feed-forward neural networks: a comparative study with SVM and AdaBoost

Enrique Romero, Xavier Carreras, Lluís Màrquez i Villodre, 2004, Neurocomputing.

Using the Leader Algorithm with Support Vector Machines for Large Data Sets

Enrique Romero, E. Romero, 2011, ICANN.

Data and knowledge visualization with virtual reality spaces, neural networks and rough sets: application to geophysical prospecting

Enrique Romero, Julio J. Valdés, Ruben Gonzalez, 2020 .

Male and female politicians on Twitter: A machine learning approach

Lluís Padró, E. Romero, A. Gallego, 2020 .

Interpreting response to TMZ therapy in murine GL261 glioblastoma by combining Radiomics, Convex-NMF and feature selection in MRI/MRSI data analysis

Margarida Julià-Sapé, Alfredo Vellido, Enrique Romero, 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

On the use of Pairwise Distance Learning for Brain Signal Classification with Limited Observations

Rui Henriques, Enrique Romero, David Calhas, 2019, Artif. Intell. Medicine.

Robust discrimination of glioblastomas from metastatic brain tumors on the basis of single‐voxel 1H MRS

C Arús, A Vellido, E Romero, 2012, NMR in biomedicine.

Weighted Contrastive Divergence

David Buchaca Prats, Jordi Delgado, Ferran Mazzanti, 2018, Neural Networks.

Performing Feature Selection With Multilayer Perceptrons

Josep M. Sopena, Enrique Romero, J. Sopena, 2008, IEEE Transactions on Neural Networks.

On the selection of hidden neurons with heuristic search strategies for approximation

Lluís A. Belanche Muñoz, Enrique Romero, Ignacio Barrio, 2006, ESANN.

Feature Selection and Outliers Detection with Genetic Algorithms and Neural Networks

Josep M. Sopena, Josep Domingo-Ferrer, René Alquézar, 2005, CCIA.

Feature and model selection with discriminatory visualization for diagnostic classification of brain tumors

Lluís A. Belanche Muñoz, Félix Fernando González-Navarro, Margarida Julià-Sapé, 2010, Neurocomputing.

A sequential algorithm for feed-forward neural networks with optimal coefficients and interacting frequencies

Enrique Romero, René Alquézar, E. Romero, 2006, Neurocomputing.

Data and knowledge visualization with virtual reality spaces, neural networks and rough sets: Application to cancer and geophysical prospecting data

Enrique Romero, Julio J. Valdés, Alan J. Barton, 2012 .

Neural Network Based Virtual Reality Spaces for Visual Data Mining of Cancer Data: An Unsupervised Perspective

Enrique Romero, Julio J. Valdés, Alan J. Barton, 2007, IWANN.

ECG assessment based on neural networks with pretraining

Enrique Romero, Vicent J. Ribas, Anna Wojdel, 2016, Appl. Soft Comput..

Incremental and Decremental Learning for Linear Support Vector Machines

Lluís A. Belanche Muñoz, Enrique Romero, Ignacio Barrio, 2007, ICANN.

Identifying Useful Human Correction Feedback from an On-Line Machine Translation Service

Alberto Barrón-Cedeño, Lluís Màrquez i Villodre, Enrique Romero, 2013, IJCAI.

Classification of human brain tumours from MRS data using Discrete Wavelet Transform and Bayesian Neural Networks

Alfredo Vellido, Enrique Romero, Carlos Arizmendi, 2012, Expert Syst. Appl..

Binary classification of brain tumours using a Discrete Wavelet Transform and energy criteria

Alfredo Vellido, Enrique Romero, Carlos Arizmendi, 2011, 2011 IEEE Second Latin American Symposium on Circuits and Systems (LASCAS).

Preprocessing MRS Information for Classification of Human Brain Tumours

Alfredo Vellido, Carlos Arizmendi, Enrique Romero, 2012 .

An experimental study of several decision issues for feature selection with multi-layer perceptrons

E. Romero, J.M. Sopena, J. Sopena, 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

Performing Feature Selection

Josep M. Sopena, Enrique Romero, J. Sopena, 2008 .

An Experimental Study of SeveralDecisionIssues forFeature Selection withMulti-Layer Perceptrons

E. Romero, J. M. Sopena, J. Sopena, 2005 .

Using machine learning tools for protein database biocuration assistance

Alfredo Vellido, René Alquézar, Caroline König, 2018, Scientific Reports.

Outlier exploration and diagnostic classification of a multi-centre 1H-MRS brain tumour database

Lluís A. Belanche Muñoz, Félix Fernando González-Navarro, Margarida Julià-Sapé, 2009, Neurocomputing.

Automated classification of brain tumours from short echo time in vivo MRS data using Gaussian Decomposition and Bayesian Neural Networks

Enrique Romero, Alfredo Vellido, Daniel A. Sierra, 2014, Expert Syst. Appl..

Assessment of electrocardiograms with pretraining and shallow networks

Enrique Romero, Vicent J. Ribas, Anna Wojdel, 2014, Computing in Cardiology 2014.

Data and Knowledge Visualization with Virtual Reality Spaces , Neural Networks and Rough Sets : Application to Geophysical Prospecting

E. Romero, J. J. Valdés, R. González, 2007 .

Frequency Selection for the Diagnostic Characterization of Human Brain Tumours

Enrique Romero, Alfredo Vellido, Carlos Arizmendi, 2009, CCIA.

Feature Selection with Single-Layer Perceptrons for a Multicentre 1H-MRS Brain Tumour Database

Josep M. Sopena, Enrique Romero, Alfredo Vellido, 2009, IWANN.

Classification, Dimensionality Reduction, and Maximally Discriminatory Visualization of a Multicentre 1H-MRS Database of Brain Tumors

Paulo J. G. Lisboa, Margarida Julià-Sapé, Carles Arús, 2008, 2008 Seventh International Conference on Machine Learning and Applications.

Brain tumour classification using Gaussian decomposition and neural networks

Enrique Romero, Alfredo Vellido, Daniel A. Sierra, 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Diagnosis of brain tumours from magnetic resonance spectroscopy using wavelets and Neural Networks

Francisco del Pozo, Alfredo Vellido, Enrique Romero, 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

Exploratory Characterization of Outliers in a Multi-centre 1H-MRS Brain Tumour Dataset

Margarida Julià-Sapé, Carles Arús, Alfredo Vellido, 2008, KES.

Comparing Support Vector Machines and Feedforward Neural Networks With Similar Hidden-Layer Weights

Enrique Romero, Daniel Toppo, E. Romero, 2007, IEEE Transactions on Neural Networks.

Feature selection forcing overtraining may help to improve performance

E. Romero, J.M. Sopena, G. Navarrete, 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

Maximizing the margin with feedforward neural networks

E. Romero, R. Alquezar, E. Romero, 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

Efficient Evaluation of the Partition Function of RBMs with Annealed Importance Sampling pdf

Enrique Romero, Ferran Mazzanti, E. Romero, 2020, ArXiv.

ECG assessment based on neural networks with pretraining

J. Brugada, V. Ribas, E. Romero, 2016, Appl. Soft Comput..

On-The-Fly Syntheziser Programming with Fuzzy Rule Learning

Enrique Romero, Àngela Nebot, Francisco Mugica, 2020, Entropy.