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Inés María Galván
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2020 – today
- 2023
- [j31]Antonio Alcántara
, Inés María Galván
Deep neural networks for the quantile estimation of regional renewable energy production. Appl. Intell. 53(7): 8318-8353 (2023) - [j30]Esteban García-Cuesta
A combination of supervised dimensionality reduction and learning methods to forecast solar radiation. Appl. Intell. 53(11): 13053-13066 (2023) - [j29]Antonio Alcántara, Inés María Galván
Pareto Optimal Prediction Intervals with Hypernetworks. Appl. Soft Comput. 133: 109930 (2023) - [j28]Miguel López-Cuesta, Ricardo Aler
Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques. Remote. Sens. 15(9): 2328 (2023) - 2022
- [j27]Antonio Alcántara, Inés María Galván
Direct estimation of prediction intervals for solar and wind regional energy forecasting with deep neural networks. Eng. Appl. Artif. Intell. 114: 105128 (2022) - [j26]Javier Huertas-Tato
Using a Multi-view Convolutional Neural Network to monitor solar irradiance. Neural Comput. Appl. 34(13): 10295-10307 (2022) - 2021
- [j25]Inés María Galván
Evolutionary-based prediction interval estimation by blending solar radiation forecasting models using meteorological weather types. Appl. Soft Comput. 109: 107531 (2021) - [j24]José María Valls
Supervised data transformation and dimensionality reduction with a 3-layer multi-layer perceptron for classification problems. J. Ambient Intell. Humaniz. Comput. 12(12): 10515-10527 (2021)
2010 – 2019
- 2018
- [j23]Kaouter Labed
A Comparative Study of Classical Clustering Method and Cuckoo Search Approach for Satellite Image Clustering: Application to Water Body Extraction. Appl. Artif. Intell. 32(1): 96-118 (2018) - [j22]Ricardo Martín
A filter attribute selection method based on local reliable information. Appl. Intell. 48(1): 35-45 (2018) - [c31]Javier Huertas-Tato
Predicting Global Irradiance Combining Forecasting Models Through Machine Learning. HAIS 2018: 622-633 - [c30]Rubén Martín-Vázquez, Javier Huertas-Tato, Ricardo Aler
Studying the Effect of Measured Solar Power on Evolutionary Multi-objective Prediction Intervals. IDEAL (2) 2018: 155-162 - [c29]Rubén Martín-Vázquez, Ricardo Aler
Wind Energy Forecasting at Different Time Horizons with Individual and Global Models. AIAI 2018: 240-248 - 2017
- [j21]Inés María Galván
Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks. Inf. Sci. 418: 363-382 (2017) - [c28]Rubén Martín-Vázquez, Ricardo Aler
A Study on Feature Selection Methods for Wind Energy Prediction. IWANN (1) 2017: 698-707 - 2016
- [j20]R. Martin, Ricardo Aler
Machine learning techniques for daily solar energy prediction and interpolation using numerical weather models. Concurr. Comput. Pract. Exp. 28(4): 1261-1274 (2016) - 2015
- [j19]Ricardo Aler
Optimizing the number of electrodes and spatial filters for Brain-Computer Interfaces by means of an evolutionary multi-objective approach. Expert Syst. Appl. 42(15-16): 6215-6223 (2015) - 2014
- [j18]Sandra García-Rodríguez, David Quintana
Extended mean-variance model for reliable evolutionary portfolio optimization. AI Commun. 27(3): 315-324 (2014) - [c27]Ricardo Aler
A Study of Machine Learning Techniques for Daily Solar Energy Forecasting Using Numerical Weather Models. IDC 2014: 269-278 - 2013
- [j17]Sandra García-Rodríguez, David Quintana, Inés María Galván, Pedro Isasi:
Multiobjective Algorithms with Resampling for Portfolio Optimization. Comput. Informatics 32(4): 777-796 (2013) - 2012
- [j16]Sandra García-Rodríguez, David Quintana
Time-stamped resampling for robust evolutionary portfolio optimization. Expert Syst. Appl. 39(12): 10722-10730 (2012) - [j15]Ricardo Aler
Applying evolution strategies to preprocessing EEG signals for brain-computer interfaces. Inf. Sci. 215: 53-66 (2012) - 2011
- [j14]Inés María Galván
A lazy learning approach for building classification models. Int. J. Intell. Syst. 26(8): 773-786 (2011) - [j13]Esteban García-Cuesta
Recursive Discriminant Regression Analysis to Find Homogeneous Groups. Int. J. Neural Syst. 21(1): 95-101 (2011) - [c26]Sandra García-Rodríguez, David Quintana
Portfolio Optimization Using SPEA2 with Resampling. IDEAL 2011: 127-134 - 2010
- [c25]Ricardo Aler
Evolving spatial and frequency selection filters for Brain-Computer Interfaces. IEEE Congress on Evolutionary Computation 2010: 1-7 - [c24]Sandra García-Rodríguez, Ricardo Aler
Using Evolutionary Multiobjective Techniques for Imbalanced Classification Data. ICANN (1) 2010: 422-427
2000 – 2009
- 2009
- [j12]Alejandro Cervantes
Michigan Particle Swarm Optimization for Prototype Reduction in Classification Problems. New Gener. Comput. 27(3): 239-257 (2009) - [j11]Alejandro Cervantes
AMPSO: A New Particle Swarm Method for Nearest Neighborhood Classification. IEEE Trans. Syst. Man Cybern. Part B 39(5): 1082-1091 (2009) - [c23]Ricardo Aler, Inés María Galván, José María Valls:
Improving Classification for Brain Computer Interfaces using Transitions and a Moving Window. BIOSIGNALS 2009: 65-71 - [c22]Ricardo Aler
Transition Detection for Brain Computer Interface Classification. BIOSTEC (Selected Papers) 2009: 200-210 - [c21]Carlos Segura
Optimizing the DFCN Broadcast Protocol with a Parallel Cooperative Strategy of Multi-Objective Evolutionary Algorithms. EMO 2009: 305-319 - [c20]Esteban García-Cuesta
Discriminant Regression Analysis to Find Homogeneous Structures. IDEAL 2009: 191-199 - [c19]Inés María Galván
A Lazy Approach for Machine Learning Algorithms. AIAI 2009: 517-522 - [c18]Sandra García-Rodríguez, Cristóbal Luque, Alejandro Cervantes
Multiobjective Algorithms Hybridization to Optimize Broadcasting Parameters in Mobile Ad-Hoc Networks. IWANN (1) 2009: 728-735 - [c17]Esteban García-Cuesta
Supervised clustering via principal component analysis in a retrieval application. KDD Workshop on Knowledge Discovery from Sensor Data 2009: 97-104 - 2008
- [j10]Esteban García-Cuesta
Multilayer perceptron as inverse model in a ground-based remote sensing temperature retrieval problem. Eng. Appl. Artif. Intell. 21(1): 26-34 (2008) - [j9]José María Valls
Learning radial basis neural networks in a lazy way: A comparative study. Neurocomputing 71(13-15): 2529-2537 (2008) - 2007
- [j8]José María Valls, Inés María Galván, Pedro Isasi:
LRBNN: A Lazy Radial Basis Neural Network model. AI Commun. 20(2): 71-86 (2007) - [c16]Alejandro Cervantes
An Adaptive Michigan Approach PSO for Nearest Prototype Classification. IWINAC (2) 2007: 287-296 - [c15]Alejandro Cervantes
Building Nearest Prototype Classifiers Using a Michigan Approach PSO. SIS 2007: 135-140 - 2006
- [j7]José María Valls, Inés María Galván, Pedro Isasi Viñuela:
Improving the Generalization Ability of RBNN Using a Selective Strategy Based on the Gaussian Kernel Function. Comput. Artif. Intell. 25(1): 1-15 (2006) - [c14]José María Valls
Lazy Training of Radial Basis Neural Networks. ICANN (1) 2006: 198-207 - [c13]Esteban García-Cuesta, Inés María Galván, Antonio J. de Castro:
Spectral High Resolution Feature Selection for Retrieval of Combustion Temperature Profiles. IDEAL 2006: 754-762 - 2005
- [j6]Germán Gutiérrez, Araceli Sanchis, Pedro Isasi Viñuela, José M. Molina, Inés María Galván:
Non-Direct Encoding Method Based on Cellular Automata to Design Neural Network Architectures. Comput. Artif. Intell. 24(3): 225-247 (2005) - [c12]Alejandro Cervantes, Inés María Galván, Pedro Isasi:
A comparison between the Pittsburgh and Michigan approaches for the binary PSO algorithm. Congress on Evolutionary Computation 2005: 290-297 - [c11]Esteban García-Cuesta, Inés María Galván, Antonio J. de Castro:
Neural Networks and Spectral Feature Selection for Retrieval of Hot Gases Temperature Profiles. CIMCA/IAWTIC 2005: 81-86 - [c10]César Estébanez, José María Valls
A First Attempt at Constructing Genetic Programming Expressions for EEG Classification. ICANN (1) 2005: 665-670 - 2004
- [j5]José María Valls, Inés María Galván, Pedro Isasi:
Lazy Learning in Radial Basis Neural Networks: A Way of Achieving More Accurate Models. Neural Process. Lett. 20(2): 105-124 (2004) - 2003
- [c9]José María Valls, Inés María Galván, Pedro Isasi:
How the Selection of Training Patterns can Improve the Generalization Capability in Radial Basis Neural Networks. Applied Informatics 2003: 275-280 - [c8]Juan Manuel Alonso-Weber, Inés María Galván, Araceli Sanchis de Miguel:
Modified Self-organizing Maps for Line Extraction in Digitized Text Documents. Applied Informatics 2003: 281-286 - [c7]Pedro Isasi
A Better Selection of Patterns in Lazy Learning Radial Basis Neural Networks. IWANN (1) 2003: 278-285 - 2002
- [c6]M. A. Guinea, Germán Gutiérrez, Inés María Galván
Generative capacities of grammars codification for evolution of NN architectures. IEEE Congress on Evolutionary Computation 2002: 611-616 - [c5]Germán Gutiérrez, Inés María Galván, José M. Molina, Araceli Sanchis:
Generative Capacities of Cellular Automata Codification for Evolution of NN Codification. ICANN 2002: 314-322 - 2001
- [j4]José M. Molina, Inés María Galván, José María Valls, Andrés Leal:
Optimizing the Number of Learning Cycles in the Design of Radial Basis Neural Networks Using a Multi-Agent System. Comput. Artif. Intell. 20(5): 429-449 (2001) - [j3]Inés María Galván, Pedro Isasi, Ricardo Aler, José María Valls:
A Selective Learning Method to Improve the Generalization of Multilayer Feedforward Neural Networks. Int. J. Neural Syst. 11(2): 167-177 (2001) - [j2]Inés María Galván
Multi-step Learning Rule for Recurrent Neural Models: An Application to Time Series Forecasting. Neural Process. Lett. 13(2): 115-133 (2001) - [c4]José María Valls
Deferring the Learning for Better Generalization in Radial Basis Neural Networks. ICANN 2001: 189-195 - [c3]Germán Gutiérrez, Pedro Isasi
Evolutionary Cellular Configurations for Designing Feed-Forward Neural Networks Architectures. IWANN (1) 2001: 514-521 - 2000
- [j1]José María Valls, José M. Molina, Inés María Galván:
Sistema Multiagente para el diseño de Redes de Neuronas de Base Radial Óptimas. Inteligencia Artif. 4(10): 18-25 (2000) - [c2]José M. Molina, Inés María Galván, Pedro Isasi, Araceli Sanchis:
Grammars and cellular automata for evolving neural networks architectures. SMC 2000: 2497-2502
1990 – 1999
- 1997
- [b1]Inés María Galván:
Nuevos modelos de redes de neuronas artificiales para simulación y control desistemas dinámicos. Technical University of Madrid, Spain, 1997 - [c1]D. Barrios, Inés María Galván, P. Jsasi, J. Ríos:
Improving Inverse Control on Real Time by Means of Neural Networks. ICONIP (2) 1997: 979-982
Coauthor Index
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