Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Mathematics
10
Scopus Publications
224
Scholar Citations
6
Scholar h-index
4
Scholar i10-index
Scopus Publications
Companion Losses for Ordinal Regression David Díaz-Vico, Angela Fernández, José R. Dorronsoro Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2022
Companion Losses for Deep Neural Networks David Díaz-Vico, Angela Fernández, José R. Dorronsoro Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2021
Deep least squares fisher discriminant analysis David Diaz-Vico, Jose R. Dorronsoro IEEE Transactions on Neural Networks and Learning Systems, 2020 While being one of the first and most elegant tools for dimensionality reduction, Fisher linear discriminant analysis (FLDA) is not currently considered among the top methods for feature extraction or classification. In this paper, we will review two recent approaches to FLDA, namely, least squares Fisher discriminant analysis (LSFDA) and regularized kernel FDA (RKFDA) and propose deep FDA (DFDA), a straightforward nonlinear extension of LSFDA that takes advantage of the recent advances on deep neural networks. We will compare the performance of RKFDA and DFDA on a large number of two-class and multiclass problems, many of them involving class-imbalanced data sets and some having quite large sample sizes; we will use, for this, the areas under the receiver operating characteristics (ROCs) curve of the classifiers considered. As we shall see, the classification performance of both methods is often very similar and particularly good on imbalanced problems, but building DFDA models is considerably much faster than doing so for RKFDA, particularly in problems with quite large sample sizes.
Deep support vector neural networks David Díaz-Vico, Jesús Prada, Adil Omari, José Dorronsoro Integrated Computer Aided Engineering, 2020 Kernel based Support Vector Machines, SVM, one of the most popular machine learning models, usually achieve top performances in two-class classification and regression problems. However, their training cost is at least quadratic on sample size, making them thus unsuitable for large sample problems. However, Deep Neural Networks (DNNs), with a cost linear on sample size, are able to solve big data problems relatively easily. In this work we propose to combine the advanced representations that DNNs can achieve in their last hidden layers with the hinge and ϵ insensitive losses that are used in two-class SVM classification and regression. We can thus have much better scalability while achieving performances comparable to those of SVMs. Moreover, we will also show that the resulting Deep SVM models are competitive with standard DNNs in two-class classification problems but have an edge in regression ones.
Deep Support Vector Classification and Regression David Díaz-Vico, Jesús Prada, Adil Omari, José R. Dorronsoro Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
Deep MLPs for Imbalanced Classification David Diaz-Vico, Anibal R. Figueiras-Vidal, Jose R. Dorronsoro Proceedings of the International Joint Conference on Neural Networks, 2018 –Classification over imbalanced datasets is a highly interesting topic given that many real-world classification problems present a concrete class with a much smaller number of patterns than the others. In this work we shall explore the use of large, fully connected and potentially deep MLPs in such problems. We will consider simple MLPs, with ReLU activations, softmax outputs and categorical cross-entropy loss, showing that, when properly regularized, these relatively straightforward MLP models yield state of the art results in terms of the areas under the ROC curve for both two-class problems (the usual focus in imbalanced classification) as well as for multi-class problems.
Deep fisher discriminant analysis David Díaz-Vico, Adil Omari, Alberto Torres-Barrán, José Ramón Dorronsoro Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017
Deep neural networks for wind energy prediction David Díaz, Alberto Torres, José R. Dorronsoro Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2015
Sparse one hidden layer MLPs 22nd European Symposium on Artificial Neural Networks Computational Intelligence and Machine Learning Esann 2014 Proceedings, 2014
RECENT SCHOLAR PUBLICATIONS
Companion losses for ordinal regression D Díaz-Vico, A Fernández, JR Dorronsoro International Conference on Hybrid Artificial Intelligence Systems, 211-222 , 2022 2022 Citations: 1
Deep learning applied to regression, classification and feature transformation problems D Díaz Vico Deep learning applied to regression, classification and feature … , 2022 2022
Companion losses for deep neural networks D Díaz-Vico, A Fernández, JR Dorronsoro International Conference on Hybrid Artificial Intelligence Systems, 538-549 , 2021 2021 Citations: 4
Deep support vector neural networks D Diaz-Vico, J Prada, A Omari, J Dorronsoro Integrated Computer-Aided Engineering 27 (4), 389-402 , 2020 2020 Citations: 36
Deep support vector classification and regression D Díaz-Vico, J Prada, A Omari, JR Dorronsoro International Work-Conference on the Interplay Between Natural and … , 2019 2019 Citations: 9
Deep least squares Fisher discriminant analysis D Díaz-Vico, JR Dorronsoro IEEE transactions on neural networks and learning systems 31 (8), 2752-2763 , 2019 2019 Citations: 45
Deep mlps for imbalanced classification D Díaz-Vico, AR Figueiras-Vidal, JR Dorronsoro 2018 International Joint Conference on Neural Networks (IJCNN), 1-7 , 2018 2018 Citations: 17
Deep neural networks for wind and solar energy prediction D Díaz–Vico, A Torres–Barrán, A Omari, JR Dorronsoro Neural Processing Letters 46 (3), 829-844 , 2017 2017 Citations: 103
Deep Fisher discriminant analysis D Díaz-Vico, A Omari, A Torres-Barrán, JR Dorronsoro International Work-Conference on Artificial Neural Networks, 501-512 , 2017 2017 Citations: 8
Procedimiento y dispositivo para localizar actividad de red en redes de comunicación celular RML Miguel A. Rodríguez-Crespo, David Díaz-Vico ES Patent ES 2596705 T3 , 2017 2017
Method and device for locating network activity in cellular communication networks RML Miguel A. Rodríguez-Crespo, David Díaz-Vico EP Patent EP 2869622 B1 , 2016 2016
Method and device for locating network activity in cellular communication networks RML Miguel A. Rodríguez-Crespo, David Díaz-Vico US Patent US 9277410 B2 , 2016 2016
Deep neural networks for wind energy prediction D Díaz-Vico, A Torres, JR Dorronsoro Ibero Lecture Notes in Computer Science (including subseries Lecture Notes in … , 2015 2015 Citations: 1
Sparse one hidden layer MLPs JRD Alberto Torres, David Díaz ESANN , 2014 2014
Deep neural networks D Díaz Vico Universidad Autónoma de Madrid , 2012 2012
MOST CITED SCHOLAR PUBLICATIONS
Deep neural networks for wind and solar energy prediction D Díaz–Vico, A Torres–Barrán, A Omari, JR Dorronsoro Neural Processing Letters 46 (3), 829-844 , 2017 2017 Citations: 103
Deep least squares Fisher discriminant analysis D Díaz-Vico, JR Dorronsoro IEEE transactions on neural networks and learning systems 31 (8), 2752-2763 , 2019 2019 Citations: 45
Deep support vector neural networks D Diaz-Vico, J Prada, A Omari, J Dorronsoro Integrated Computer-Aided Engineering 27 (4), 389-402 , 2020 2020 Citations: 36
Deep mlps for imbalanced classification D Díaz-Vico, AR Figueiras-Vidal, JR Dorronsoro 2018 International Joint Conference on Neural Networks (IJCNN), 1-7 , 2018 2018 Citations: 17
Deep support vector classification and regression D Díaz-Vico, J Prada, A Omari, JR Dorronsoro International Work-Conference on the Interplay Between Natural and … , 2019 2019 Citations: 9
Deep Fisher discriminant analysis D Díaz-Vico, A Omari, A Torres-Barrán, JR Dorronsoro International Work-Conference on Artificial Neural Networks, 501-512 , 2017 2017 Citations: 8
Companion losses for deep neural networks D Díaz-Vico, A Fernández, JR Dorronsoro International Conference on Hybrid Artificial Intelligence Systems, 538-549 , 2021 2021 Citations: 4
Companion losses for ordinal regression D Díaz-Vico, A Fernández, JR Dorronsoro International Conference on Hybrid Artificial Intelligence Systems, 211-222 , 2022 2022 Citations: 1
Deep neural networks for wind energy prediction D Díaz-Vico, A Torres, JR Dorronsoro Ibero Lecture Notes in Computer Science (including subseries Lecture Notes in … , 2015 2015 Citations: 1
Deep learning applied to regression, classification and feature transformation problems D Díaz Vico Deep learning applied to regression, classification and feature … , 2022 2022
Procedimiento y dispositivo para localizar actividad de red en redes de comunicación celular RML Miguel A. Rodríguez-Crespo, David Díaz-Vico ES Patent ES 2596705 T3 , 2017 2017
Method and device for locating network activity in cellular communication networks RML Miguel A. Rodríguez-Crespo, David Díaz-Vico EP Patent EP 2869622 B1 , 2016 2016
Method and device for locating network activity in cellular communication networks RML Miguel A. Rodríguez-Crespo, David Díaz-Vico US Patent US 9277410 B2 , 2016 2016
Sparse one hidden layer MLPs JRD Alberto Torres, David Díaz ESANN , 2014 2014
Deep neural networks D Díaz Vico Universidad Autónoma de Madrid , 2012 2012
