default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDConcha Bielza
- > Home > Persons > Concha Bielza
Publications
- 2024
[j109]Vicente P. Soloviev
, Pedro Larrañaga, Concha Bielza:
EDAspy: An extensible python package for estimation of distribution algorithms. Neurocomputing 598: 128043 (2024)- [j108]Vicente P. Soloviev, Concha Bielza, Pedro Larrañaga:
Semiparametric Estimation of Distribution Algorithms for Continuous Optimization. IEEE Trans. Evol. Comput. 28(4): 1069-1083 (2024) - [j107]Carlos Puerto-Santana, Pedro Larrañaga, Concha Bielza:
Feature Saliencies in Asymmetric Hidden Markov Models. IEEE Trans. Neural Networks Learn. Syst. 35(3): 3586-3600 (2024) - 2023
- [j106]Carlos Villa-Blanco, Concha Bielza, Pedro Larrañaga:
Feature subset selection for data and feature streams: a review. Artif. Intell. Rev. 56(S1): 1011-1062 (2023) - [j105]Gabriel Valverde, David Quesada, Pedro Larrañaga, Concha Bielza:
Causal reinforcement learning based on Bayesian networks applied to industrial settings. Eng. Appl. Artif. Intell. 125: 106657 (2023) - [j104]Carlos Villa-Blanco, Alessandro Bregoli, Concha Bielza, Pedro Larrañaga, Fabio Stella:
Constraint-based and hybrid structure learning of multidimensional continuous-time Bayesian network classifiers. Int. J. Approx. Reason. 159: 108945 (2023) - [j103]Enrique Valero-Leal, Concha Bielza, Pedro Larrañaga, Silja Renooij:
Efficient search for relevance explanations using MAP-independence in Bayesian networks. Int. J. Approx. Reason. 160: 108965 (2023) - [j102]Carlos Puerto-Santana, Pedro Larrañaga, Concha Bielza:
Feature subset selection in data-stream environments using asymmetric hidden Markov models and novelty detection. Neurocomputing 554: 126641 (2023) - [j101]Nikolas Bernaola, Mario Michiels, Pedro Larrañaga, Concha Bielza:
Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks. PLoS Comput. Biol. 19(12) (2023) - [j100]Vicente P. Soloviev, Concha Bielza, Pedro Larrañaga:
Quantum approximate optimization algorithm for Bayesian network structure learning. Quantum Inf. Process. 22(1): 19 (2023) - [c57]Vicente P. Soloviev, Pedro Larrañaga, Concha Bielza:
Variational Quantum Algorithm Parameter Tuning with Estimation of Distribution Algorithms. CEC 2023: 1-9 - [c56]Dafne Lozano, Luis Bote, Concha Bielza, Pedro Larrañaga, María Sabater-Molina, Juan Ramón Gimeno, Sergio Muñoz, Francisco Javier Gimeno-Blanes, José Luis Rojo-Álvarez:
High-Dimensional Feature Characterization of Single Nucleotide Variants in Hypertrophic Cardiomyopathy. CinC 2023: 1-4 - [c55]Jorge Casajús-Setién, Concha Bielza, Pedro Larrañaga:
Anomaly-Based Intrusion Detection in IIoT Networks Using Transformer Models. CSR 2023: 72-77 - [i10]Carlos Puerto-Santana, Concha Bielza, Pedro Larrañaga, Gustav Eje Henter:
Context-specific kernel-based hidden Markov model for time series analysis. CoRR abs/2301.09870 (2023) - [i9]David Quesada, Pedro Larrañaga, Concha Bielza:
Classifying the evolution of COVID-19 severity on patients with combined dynamic Bayesian networks and neural networks. CoRR abs/2303.05972 (2023) - 2022
- [j99]Fernando Rodriguez-Sanchez, Concha Bielza, Pedro Larrañaga:
Multipartition clustering of mixed data with Bayesian networks. Int. J. Intell. Syst. 37(3): 2188-2218 (2022) - [j98]David Quesada, Concha Bielza, Pedro Fontán, Pedro Larrañaga:
Piecewise forecasting of nonlinear time series with model tree dynamic Bayesian networks. Int. J. Intell. Syst. 37(11): 9108-9137 (2022) - [j97]David Atienza, Concha Bielza, Pedro Larrañaga:
PyBNesian: An extensible python package for Bayesian networks. Neurocomputing 504: 204-209 (2022) - [j96]Carlos Puerto-Santana, Concha Bielza, Javier Diaz-Rozo, Guillem Ramirez-Gargallo, Filippo Mantovani, Gaizka Virumbrales, Jesús Labarta, Pedro Larrañaga:
Asymmetric HMMs for Online Ball-Bearing Health Assessments. IEEE Internet Things J. 9(20): 20160-20177 (2022) - [j95]David Atienza, Concha Bielza, Pedro Larrañaga:
Semiparametric Bayesian networks. Inf. Sci. 584: 564-582 (2022) - [j94]Vicente P. Soloviev, Pedro Larrañaga, Concha Bielza:
Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables. J. Comb. Optim. 44(2): 1077-1098 (2022) - [j93]Carlos Puerto-Santana, Pedro Larrañaga, Concha Bielza:
Autoregressive Asymmetric Linear Gaussian Hidden Markov Models. IEEE Trans. Pattern Anal. Mach. Intell. 44(9): 4642-4658 (2022) - [c54]Vicente P. Soloviev, Pedro Larrañaga, Concha Bielza:
Quantum parametric circuit optimization with estimation of distribution algorithms. GECCO Companion 2022: 2247-2250 - [c53]Carlos Villa-Blanco, Alessandro Bregoli, Concha Bielza, Pedro Larrañaga, Fabio Stella:
Structure learning algorithms for multidimensional continuous-time Bayesian network classifiers. PGM 2022: 313-324 - [c52]Enrique Valero-Leal, Pedro Larrañaga, Concha Bielza:
Interpreting Time-Varying Dynamic Bayesian Networks for Earth Climate Modelling. PGM 2022: 373-384 - [c51]Jorge Casajús-Setién, Concha Bielza, Pedro Larrañaga:
Evolutive Adversarially-Trained Bayesian Network Autoencoder for Interpretable Anomaly Detection. PGM 2022: 397-408 - [d1]David Atienza, Concha Bielza, Javier Diaz-Rozo, Pedro Larrañaga:
Anomaly Detection with Laser Heat Treatment Thermal Videos. IEEE DataPort, 2022
- [i8]Vicente P. Soloviev, Concha Bielza, Pedro Larrañaga:
Quantum Approximate Optimization Algorithm for Bayesian network structure learning. CoRR abs/2203.02400 (2022) - 2021
- [j92]Santiago Gil-Begue, Concha Bielza, Pedro Larrañaga:
Multi-dimensional Bayesian network classifiers: A survey. Artif. Intell. Rev. 54(1): 519-559 (2021) - [j91]David Quesada, Gabriel Valverde, Pedro Larrañaga, Concha Bielza:
Long-term forecasting of multivariate time series in industrial furnaces with dynamic Gaussian Bayesian networks. Eng. Appl. Artif. Intell. 103: 104301 (2021) - [j90]Bojan Mihaljevic, Pedro Larrañaga, Concha Bielza:
Comparing the Electrophysiology and Morphology of Human and Mouse Layer 2/3 Pyramidal Neurons With Bayesian Networks. Frontiers Neuroinformatics 15: 580873 (2021) - [j89]Carlos Villa-Blanco, Pedro Larrañaga, Concha Bielza:
Multidimensional continuous time Bayesian network classifiers. Int. J. Intell. Syst. 36(12): 7839-7866 (2021) - [j88]Mario Michiels, Pedro Larrañaga, Concha Bielza:
BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience. Neurocomputing 428: 166-181 (2021) - [j87]Bojan Mihaljevic, Concha Bielza, Pedro Larrañaga:
Bayesian networks for interpretable machine learning and optimization. Neurocomputing 456: 648-665 (2021) - [c50]Vicente P. Soloviev, Concha Bielza, Pedro Larrañaga:
Quantum-Inspired Estimation Of Distribution Algorithm To Solve The Travelling Salesman Problem. CEC 2021: 416-425 - [c49]David Quesada, Concha Bielza, Pedro Larrañaga:
Structure Learning of High-Order Dynamic Bayesian Networks via Particle Swarm Optimization with Order Invariant Encoding. HAIS 2021: 158-171 - [c48]Carlos Puerto-Santana, Pedro Larrañaga, Javier Diaz-Rozo, Concha Bielza:
An Online Feature Selection Methodology for Ball-Bearing Harmonic Frequencies Based on HMMs. SOCO 2021: 546-555 - [i7]David Atienza, Concha Bielza, Pedro Larrañaga:
Semiparametric Bayesian Networks. CoRR abs/2109.03008 (2021) - 2020
- [j86]Irene Córdoba, Concha Bielza, Pedro Larrañaga, Gherardo Varando:
Sparse Cholesky Covariance Parametrization for Recovering Latent Structure in Ordered Data. IEEE Access 8: 154614-154624 (2020) - [j85]Fernando Rodriguez-Sanchez, Pedro Larrañaga, Concha Bielza:
Incremental Learning of Latent Forests. IEEE Access 8: 224420-224432 (2020) - [j84]Javier Diaz-Rozo, Concha Bielza, Pedro Larrañaga:
Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering. Eng. Appl. Artif. Intell. 89: 103434 (2020) - [j83]Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga:
On generating random Gaussian graphical models. Int. J. Approx. Reason. 125: 240-250 (2020) - [c47]Nikolas Bernaola, Mario Michiels, Concha Bielza, Pedro Larrañaga:
BayesSuites: An Open Web Framework for Visualization of Massive Bayesian Networks. PGM 2020: 593-596 - [i6]Irene Córdoba, Concha Bielza, Pedro Larrañaga, Gherardo Varando:
Sparse Cholesky covariance parametrization for recovering latent structure in ordered data. CoRR abs/2006.01448 (2020) - [i5]Carlos Puerto-Santana, Pedro Larrañaga, Concha Bielza:
Autoregressive Asymmetric Linear Gaussian Hidden Markov Models. CoRR abs/2010.15604 (2020) - 2019
- [j82]Pablo Fernandez-Gonzalez, Concepcion Bielza, Pedro Larrañaga:
Random Forests for Regression as a Weighted Sum of ${k}$ -Potential Nearest Neighbors. IEEE Access 7: 25660-25672 (2019) - [j81]Sergio Luengo-Sanchez, Pedro Larrañaga, Concha Bielza:
A Directional-Linear Bayesian Network and Its Application for Clustering and Simulation of Neural Somas. IEEE Access 7: 69907-69921 (2019) - [j80]Marco Benjumeda, Concha Bielza, Pedro Larrañaga:
Learning tractable Bayesian networks in the space of elimination orders. Artif. Intell. 274: 66-90 (2019) - [j79]Ignacio Leguey, Concha Bielza, Pedro Larrañaga:
Circular Bayesian classifiers using wrapped Cauchy distributions. Data Knowl. Eng. 122: 101-115 (2019) - [j78]Ignacio Leguey, Pedro Larrañaga, Concha Bielza, Shogo Kato:
A circular-linear dependence measure under Johnson-Wehrly distributions and its application in Bayesian networks. Inf. Sci. 486: 240-253 (2019) - [j77]Marco Benjumeda, Sergio Luengo-Sanchez, Pedro Larrañaga, Concha Bielza:
Tractable learning of Bayesian networks from partially observed data. Pattern Recognit. 91: 190-199 (2019) - 2018
- [j76]Bojan Mihaljevic, Pedro Larrañaga, Ruth Benavides-Piccione, Sean L. Hill, Javier DeFelipe, Concha Bielza:
Towards a supervised classification of neocortical interneuron morphologies. BMC Bioinform. 19(1): 511:1-511:22 (2018) - [j75]Marco Benjumeda, Concha Bielza, Pedro Larrañaga:
Tractability of most probable explanations in multidimensional Bayesian network classifiers. Int. J. Approx. Reason. 93: 74-87 (2018) - [j74]Javier Diaz-Rozo, Concha Bielza, Pedro Larrañaga:
Clustering of Data Streams With Dynamic Gaussian Mixture Models: An IoT Application in Industrial Processes. IEEE Internet Things J. 5(5): 3533-3547 (2018) - [j73]Sergio Luengo-Sanchez, Isabel Fernaud, Concha Bielza, Ruth Benavides-Piccione, Pedro Larrañaga, Javier DeFelipe:
3D morphology-based clustering and simulation of human pyramidal cell dendritic spines. PLoS Comput. Biol. 14(6) (2018) - [j72]Laura Anton-Sanchez, Felix Effenberger, Concha Bielza, Pedro Larrañaga, Hermann Cuntz:
A regularity index for dendrites - local statistics of a neuron's input space. PLoS Comput. Biol. 14(11) (2018) - [j71]Bojan Mihaljevic, Concha Bielza, Pedro Larrañaga:
bnclassify: Learning Bayesian Network Classifiers. R J. 10(2): 455 (2018) - [c46]Irene Córdoba, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, Concha Bielza, Pedro Larrañaga:
Bayesian Optimization of the PC Algorithm for Learning Gaussian Bayesian Networks. CAEPIA 2018: 44-54 - [c45]Carlos Puerto-Santana, Concha Bielza, Pedro Larrañaga:
Asymmetric Hidden Markov Models with Continuous Variables. CAEPIA 2018: 98-107 - [c44]Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga:
A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices. IDEAL (1) 2018: 117-124 - [c43]Santiago Gil-Begue, Pedro Larrañaga, Concha Bielza:
Multi-dimensional Bayesian Network Classifier Trees. IDEAL (1) 2018: 354-363 - [c42]Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga:
A partial orthogonalization method for simulating covariance and concentration graph matrices. PGM 2018: 61-72 - [c41]Bojan Mihaljevic, Concha Bielza, Pedro Larrañaga:
Learning Bayesian network classifiers with completed partially directed acyclic graphs. PGM 2018: 272-283 - [c40]Fernando Rodriguez-Sanchez, Pedro Larrañaga, Concha Bielza:
Discrete model-based clustering with overlapping subsets of attributes. PGM 2018: 392-403 - [i4]Irene Córdoba-Sánchez, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, Concha Bielza, Pedro Larrañaga:
Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks. CoRR abs/1806.11015 (2018) - [i3]Gherardo Varando, Concha Bielza, Pedro Larrañaga, Eva Riccomagno:
Markov Property in Generative Classifiers. CoRR abs/1811.04759 (2018) - [i2]Irene Córdoba, Concha Bielza, Pedro Larrañaga:
Towards Gaussian Bayesian Network Fusion. CoRR abs/1812.00262 (2018) - 2017
- [j70]Laura Anton-Sanchez, Concha Bielza, Pedro Larrañaga:
Network design through forests with degree- and role-constrained minimum spanning trees. J. Heuristics 23(1): 31-51 (2017) - [j69]Luis Rodriguez-Lujan, Pedro Larrañaga, Concha Bielza:
Frobenius Norm Regularization for the Multivariate Von Mises Distribution. Int. J. Intell. Syst. 32(2): 153-176 (2017) - [j68]Pablo Fernandez-Gonzalez, Concha Bielza, Pedro Larrañaga:
Univariate and bivariate truncated von Mises distributions. Prog. Artif. Intell. 6(2): 171-180 (2017) - [c39]Javier Mesonero, Concha Bielza, Pedro Larrañaga:
Architecture for anomaly detection in a laser heating surface process. ETFA 2017: 1-4 - 2016
- [j67]Hanen Borchani, Pedro Larrañaga, João Gama, Concha Bielza:
Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers. Intell. Data Anal. 20(2): 257-280 (2016) - [j66]Gherardo Varando, Concha Bielza, Pedro Larrañaga:
Decision functions for chain classifiers based on Bayesian networks for multi-label classification. Int. J. Approx. Reason. 68: 164-178 (2016) - [j65]Alfonso Ibáñez, Rubén Armañanzas, Concha Bielza, Pedro Larrañaga:
Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices. J. Assoc. Inf. Sci. Technol. 67(7): 1703-1721 (2016) - [j64]Laura Anton-Sanchez, Concha Bielza, Ruth Benavides-Piccione, Javier DeFelipe, Pedro Larrañaga:
Dendritic and Axonal Wiring Optimization of Cortical GABAergic Interneurons. Neuroinformatics 14(4): 453-464 (2016) - [j63]Marco Benjumeda, Pedro Larrañaga, Concha Bielza:
Learning Bayesian networks with low inference complexity. Prog. Artif. Intell. 5(1): 15-26 (2016) - [c38]Ignacio Leguey, Concha Bielza, Pedro Larrañaga:
Tree-Structured Bayesian Networks for Wrapped Cauchy Directional Distributions. CAEPIA 2016: 207-216 - [c37]Sergio Luengo-Sanchez, Concha Bielza, Pedro Larrañaga:
Hybrid Gaussian and von Mises Model-Based Clustering. ECAI 2016: 855-862 - [c36]Alberto Ogbechie, Javier Diaz-Rozo, Pedro Larrañaga, Concha Bielza:
Dynamic Bayesian Network-Based Anomaly Detection for In-Process Visual Inspection of Laser Surface Heat Treatment. ML4CPS 2016: 17-24 - [c35]Marco Benjumeda, Concha Bielza, Pedro Larrañaga:
Learning Tractable Multidimensional Bayesian Network Classifiers. Probabilistic Graphical Models 2016: 13-24 - [c34]David Atienza, Concha Bielza, Javier Díaz, Pedro Larrañaga:
Anomaly Detection with a Spatio-Temporal Tracking of the Laser Spot. STAIRS 2016: 137-142 - [i1]Irene Córdoba-Sánchez, Concha Bielza, Pedro Larrañaga:
A review of undirected and acyclic directed Gaussian Markov model selection and estimation. CoRR abs/1606.07282 (2016) - 2015
- [j62]Bojan Mihaljevic, Ruth Benavides-Piccione, Luis Guerra, Javier DeFelipe, Pedro Larrañaga, Concha Bielza:
Classifying GABAergic interneurons with semi-supervised projected model-based clustering. Artif. Intell. Medicine 65(1): 49-59 (2015) - [j61]Hossein Karshenas, Concha Bielza, Pedro Larrañaga:
Interval-based ranking in noisy evolutionary multi-objective optimization. Comput. Optim. Appl. 61(2): 517-555 (2015) - [j58]Gherardo Varando, Pedro L. López-Cruz, Thomas D. Nielsen, Pedro Larrañaga, Concha Bielza:
Conditional Density Approximations with Mixtures of Polynomials. Int. J. Intell. Syst. 30(3): 236-264 (2015) - [j57]Gherardo Varando, Concha Bielza, Pedro Larrañaga:
Decision boundary for discrete Bayesian network classifiers. J. Mach. Learn. Res. 16: 2725-2749 (2015) - [j55]Bojan Mihaljevic, Ruth Benavides-Piccione, Concha Bielza, Javier DeFelipe, Pedro Larrañaga:
Bayesian Network Classifiers for Categorizing Cortical GABAergic Interneurons. Neuroinformatics 13(2): 193-208 (2015) - [j54]Pedro L. López-Cruz, Concha Bielza, Pedro Larrañaga:
Directional naive Bayes classifiers. Pattern Anal. Appl. 18(2): 225-246 (2015) - [j53]Hanen Borchani, Gherardo Varando, Concha Bielza, Pedro Larrañaga:
A survey on multi-output regression. WIREs Data Mining Knowl. Discov. 5(5): 216-233 (2015) - [c33]Luis Rodriguez-Lujan, Concha Bielza, Pedro Larrañaga:
Regularized Multivariate von Mises Distribution. CAEPIA 2015: 25-35 - [c32]Irene Córdoba-Sánchez, Concha Bielza, Pedro Larrañaga:
Towards Gaussian Bayesian Network Fusion. ECSQARU 2015: 519-528 - [c31]Javier Díaz, Concha Bielza, Jose L. Ocaña, Pedro Larrañaga:
Development of a Cyber-Physical System based on selective Gaussian naïve Bayes model for a self-predict laser surface heat treatment process control. ML4CPS 2015: 1-8 - 2014
- [j52]Concha Bielza, Pedro Larrañaga:
Discrete Bayesian Network Classifiers: A Survey. ACM Comput. Surv. 47(1): 5:1-5:43 (2014) - [j51]Luis Guerra, Concha Bielza, Víctor Robles, Pedro Larrañaga:
Semi-supervised projected model-based clustering. Data Min. Knowl. Discov. 28(4): 882-917 (2014) - [j50]Concha Bielza, Pedro Larrañaga:
Bayesian networks in neuroscience: a survey. Frontiers Comput. Neurosci. 8: 131 (2014) - [j49]Bojan Mihaljevic, Concha Bielza, Ruth Benavides-Piccione, Javier DeFelipe, Pedro Larrañaga:
Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty. Frontiers Comput. Neurosci. 8: 150 (2014) - [j48]Pedro L. López-Cruz, Pedro Larrañaga, Javier DeFelipe, Concha Bielza:
Bayesian network modeling of the consensus between experts: An application to neuron classification. Int. J. Approx. Reason. 55(1): 3-22 (2014) - [j47]Pedro L. López-Cruz, Concha Bielza, Pedro Larrañaga:
Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation. Int. J. Approx. Reason. 55(4): 989-1010 (2014) - [j46]Alfonso Ibáñez, Concha Bielza, Pedro Larrañaga:
Cost-sensitive selective naive Bayes classifiers for predicting the increase of the h-index for scientific journals. Neurocomputing 135: 42-52 (2014) - [j45]Luis Enrique Sucar, Concha Bielza, Eduardo F. Morales, Pablo Hernandez-Leal, Julio H. Zaragoza, Pedro Larrañaga:
Multi-label classification with Bayesian network-based chain classifiers. Pattern Recognit. Lett. 41: 14-22 (2014) - [j44]Hossein Karshenas, Roberto Santana, Concha Bielza, Pedro Larrañaga:
Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables. IEEE Trans. Evol. Comput. 18(4): 519-542 (2014) - [j43]Jesse Read, Concha Bielza, Pedro Larrañaga:
Multi-Dimensional Classification with Super-Classes. IEEE Trans. Knowl. Data Eng. 26(7): 1720-1733 (2014) - [c30]Gherardo Varando, Concha Bielza, Pedro Larrañaga:
Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification. Probabilistic Graphical Models 2014: 519-534 - 2013
- [j42]Hanen Borchani, Concha Bielza, Carlos Toro, Pedro Larrañaga:
Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers. Artif. Intell. Medicine 57(3): 219-229 (2013) - [j41]Rubén Armañanzas, Concha Bielza, Kallol Ray Chaudhuri, Pablo Martínez-Martín, Pedro Larrañaga:
Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach. Artif. Intell. Medicine 58(3): 195-202 (2013) - [j40]Hossein Karshenas, Roberto Santana, Concha Bielza, Pedro Larrañaga:
Regularized continuous estimation of distribution algorithms. Appl. Soft Comput. 13(5): 2412-2432 (2013) - [j39]Diego Vidaurre, Concha Bielza, Pedro Larrañaga:
Sparse regularized local regression. Comput. Stat. Data Anal. 62: 122-135 (2013) - [j38]Diego Vidaurre, Concha Bielza, Pedro Larrañaga:
An L1-Regularized naïVE Bayes-Inspired Classifier for Discarding Redundant and Irrelevant Predictors. Int. J. Artif. Intell. Tools 22(4) (2013) - [j37]Roberto Santana, Rubén Armañanzas, Concha Bielza, Pedro Larrañaga:
Network measures for information extraction in evolutionary algorithms. Int. J. Comput. Intell. Syst. 6(6): 1163-1188 (2013) - [j36]Diego Vidaurre, Concha Bielza, Pedro Larrañaga:
Classification of neural signals from sparse autoregressive features. Neurocomputing 111: 21-26 (2013) - [j35]Miguel García-Torres, Rubén Armañanzas, Concha Bielza, Pedro Larrañaga:
Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data. Inf. Sci. 222: 229-246 (2013) - [j34]Pedro Larrañaga, Hossein Karshenas, Concha Bielza, Roberto Santana:
A review on evolutionary algorithms in Bayesian network learning and inference tasks. Inf. Sci. 233: 109-125 (2013) - [j33]Concha Bielza, Juan A. Fernández del Pozo, Pedro Larrañaga:
Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks - A Case Study for the Optimal Ordering of Tables. J. Comput. Sci. Technol. 28(4): 720-731 (2013) - [j32]Diego Vidaurre, Marcel A. J. van Gerven, Concha Bielza, Pedro Larrañaga, Tom Heskes:
Bayesian Sparse Partial Least Squares. Neural Comput. 25(12): 3318-3339 (2013) - [j31]Alfonso Ibáñez, Concha Bielza, Pedro Larrañaga:
Relationship among research collaboration, number of documents and number of citations: a case study in Spanish computer science production in 2000-2009. Scientometrics 95(2): 689-716 (2013) - [j30]Alfonso Ibáñez, Pedro Larrañaga, Concha Bielza:
Cluster methods for assessing research performance: exploring Spanish computer science. Scientometrics 97(3): 571-600 (2013) - [c29]Luis Guerra, Ruth Benavides-Piccione, Concha Bielza, Víctor Robles, Javier DeFelipe, Pedro Larrañaga:
Semi-supervised Projected Clustering for Classifying GABAergic Interneurons. AIME 2013: 156-165 - [c28]Pedro L. López-Cruz, Concha Bielza, Pedro Larrañaga:
Learning Conditional Linear Gaussian Classifiers with Probabilistic Class Labels. CAEPIA 2013: 139-148 - [c27]Bojan Mihaljevic, Pedro Larrañaga, Concha Bielza:
Augmented Semi-naive Bayes Classifier. CAEPIA 2013: 159-167 - [c26]Pedro L. López-Cruz, Thomas D. Nielsen, Concha Bielza, Pedro Larrañaga:
Learning Mixtures of Polynomials of Conditional Densities from Data. CAEPIA 2013: 363-372 - [c25]Pedro Larrañaga, Concha Bielza:
Bayesian networks to answer challenging neuroscience questions. CBMS 2013: 2 - [c24]Laura Anton-Sanchez, Concha Bielza, Pedro Larrañaga:
Towards optimal neuronal wiring through estimation of distribution algorithms. GECCO (Companion) 2013: 1647-1650 - 2012
- [j29]Roberto Santana, Concha Bielza, Pedro Larrañaga:
Regularized logistic regression and multiobjective variable selection for classifying MEG data. Biol. Cybern. 106(6-7): 389-405 (2012) - [j28]Rubén Armañanzas, Pedro Larrañaga, Concha Bielza:
Ensemble transcript interaction networks: A case study on Alzheimer's disease. Comput. Methods Programs Biomed. 108(1): 442-450 (2012) - [j27]Diego Vidaurre, Concha Bielza, Pedro Larrañaga:
Lazy lasso for local regression. Comput. Stat. 27(3): 531-550 (2012) - [j26]Pedro Larrañaga, Hossein Karshenas, Concha Bielza, Roberto Santana:
A review on probabilistic graphical models in evolutionary computation. J. Heuristics 18(5): 795-819 (2012) - [j25]Luis Guerra, Víctor Robles, Concha Bielza, Pedro Larrañaga:
A comparison of clustering quality indices using outliers and noise. Intell. Data Anal. 16(4): 703-715 (2012) - [j24]Hanen Borchani, Concha Bielza, Pablo Martínez-Martín, Pedro Larrañaga:
Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39). J. Biomed. Informatics 45(6): 1175-1184 (2012) - [j23]Diego Vidaurre, Concha Bielza, Pedro Larrañaga:
Forward stagewise naïve Bayes. Prog. Artif. Intell. 1(1): 57-69 (2012) - [c23]Roberto Santana, Concha Bielza, Pedro Larrañaga:
Maximizing the number of polychronous groups in spiking networks. GECCO (Companion) 2012: 1499-1500 - 2011
- [j21]Concha Bielza, Víctor Robles, Pedro Larrañaga:
Regularized logistic regression without a penalty term: An application to cancer classification with microarray data. Expert Syst. Appl. 38(5): 5110-5118 (2011) - [j20]Endika Bengoetxea, Pedro Larrañaga, Concha Bielza, Juan A. Fernández del Pozo:
Optimal row and column ordering to improve table interpretation using estimation of distribution algorithms. J. Heuristics 17(5): 567-588 (2011) - [j19]Hanen Borchani, Pedro Larrañaga, Concha Bielza:
Classifying evolving data streams with partially labeled data. Intell. Data Anal. 15(5): 655-670 (2011) - [j18]Concha Bielza, Guangdi Li, Pedro Larrañaga:
Multi-dimensional classification with Bayesian networks. Int. J. Approx. Reason. 52(6): 705-727 (2011) - [j17]Roberto Santana, Concha Bielza, Pedro Larrañaga:
Optimizing Brain Networks Topologies Using Multi-objective Evolutionary Computation. Neuroinformatics 9(1): 3-19 (2011) - [j16]Pedro L. López-Cruz, Concha Bielza, Pedro Larrañaga, Ruth Benavides-Piccione, Javier DeFelipe:
Models and Simulation of 3D Neuronal Dendritic Trees Using Bayesian Networks. Neuroinformatics 9(4): 347-369 (2011) - [j15]Alfonso Ibáñez, Pedro Larrañaga, Concha Bielza:
Using Bayesian networks to discover relationships between bibliometric indices. A case study of computer science and artificial intelligence journals. Scientometrics 89(2): 523-551 (2011) - [j14]Rubén Armañanzas, Yvan Saeys, Iñaki Inza, Miguel García-Torres, Concha Bielza, Yves Van de Peer, Pedro Larrañaga:
Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms. IEEE ACM Trans. Comput. Biol. Bioinform. 8(3): 760-774 (2011) - [c22]Pedro L. López-Cruz, Concha Bielza, Pedro Larrañaga:
The von Mises Naive Bayes Classifier for Angular Data. CAEPIA 2011: 145-154 - [c21]Hossein Karshenas, Roberto Santana, Concha Bielza, Pedro Larrañaga:
Multi-objective Optimization with Joint Probabilistic Modeling of Objectives and Variables. EMO 2011: 298-312 - [c20]Roberto Santana, Hossein Karshenas, Concha Bielza, Pedro Larrañaga:
Quantitative genetics in multi-objective optimization algorithms: from useful insights to effective methods. GECCO (Companion) 2011: 91-92 - [c19]Roberto Santana, Concha Bielza, Pedro Larrañaga:
Affinity propagation enhanced by estimation of distribution algorithms. GECCO 2011: 331-338 - [c18]Roberto Santana, Hossein Karshenas, Concha Bielza, Pedro Larrañaga:
Regularized k-order markov models in EDAs. GECCO 2011: 593-600 - [c17]Julio H. Zaragoza, Luis Enrique Sucar, Eduardo F. Morales, Concha Bielza, Pedro Larrañaga:
Bayesian Chain Classifiers for Multidimensional Classification. IJCAI 2011: 2192-2197 - [c16]Alfonso Ibáñez, Pedro Larrañaga, Concha Bielza:
Predicting the h-index with cost-sensitive naive Bayes. ISDA 2011: 599-604 - 2010
- [j12]Concha Bielza, Juan A. Fernández del Pozo, Pedro Larrañaga, Endika Bengoetxea:
Multidimensional statistical analysis of the parameterization of a genetic algorithm for the optimal ordering of tables. Expert Syst. Appl. 37(1): 804-815 (2010) - [j11]Diego Vidaurre, Concha Bielza, Pedro Larrañaga:
Learning an L1-Regularized Gaussian Bayesian Network in the Equivalence Class Space. IEEE Trans. Syst. Man Cybern. Part B 40(5): 1231-1242 (2010) - [c15]Alfredo Cuesta-Infante, Roberto Santana, José Ignacio Hidalgo, Concha Bielza, Pedro Larrañaga:
Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms. IEEE Congress on Evolutionary Computation 2010: 1-8 - [c14]Roberto Santana, Concha Bielza, Pedro Larrañaga:
Using Probabilistic Dependencies Improves the Search of Conductance-Based Compartmental Neuron Models. EvoBIO 2010: 170-181 - [c13]Roberto Santana, Concha Bielza, Pedro Larrañaga:
Synergies between Network-Based Representation and Probabilistic Graphical Models for Classification, Inference and Optimization Problems in Neuroscience. IEA/AIE (3) 2010: 149-158 - [c12]Hanen Borchani, Pedro Larrañaga, Concha Bielza:
Mining Concept-Drifting Data Streams Containing Labeled and Unlabeled Instances. IEA/AIE (1) 2010: 531-540 - 2009
- [j10]Alfonso Ibáñez, Pedro Larrañaga, Concha Bielza:
Predicting citation count of Bioinformatics papers within four years of publication. Bioinform. 25(24): 3303-3309 (2009) - [c11]Roberto Santana, Concha Bielza, José Antonio Lozano, Pedro Larrañaga:
Mining probabilistic models learned by EDAs in the optimization of multi-objective problems. GECCO 2009: 445-452 - [c10]Elva Díaz, Eunice Ponce de León, Pedro Larrañaga, Concha Bielza:
Probabilistic Graphical Markov Model Learning: An Adaptive Strategy. MICAI 2009: 225-236 - 2008
- [j8]Rubén Armañanzas, Iñaki Inza, Roberto Santana, Yvan Saeys, Jose Luis Flores, José Antonio Lozano, Yves Van de Peer, Rosa Blanco, Víctor Robles, Concha Bielza, Pedro Larrañaga:
A review of estimation of distribution algorithms in bioinformatics. BioData Min. 1 (2008) - 2006
- [j5]Pedro Larrañaga, Borja Calvo, Roberto Santana, Concha Bielza, Josu Galdiano, Iñaki Inza, José Antonio Lozano, Rubén Armañanzas, Guzmán Santafé, Aritz Pérez Martínez, Victor Robles:
Machine learning in bioinformatics. Briefings Bioinform. 7(1): 86-112 (2006)
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-08-23 19:28 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
