Pedro Larrañaga

发表

Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks

Irene Córdoba-Sánchez, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, 2018, CAEPIA.

Structure learning approaches in Causal Probalistics Networks

Pedro Larrañaga, Yosu Yurramendi, 1993, ECSQARU.

Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms

Pedro Larrañaga, Jose A. Lozano, Iñaki Inza, 2006, Towards a New Evolutionary Computation.

Using Bayesian networks to discover relationships between bibliometric indices. A case study of computer science and artificial intelligence journals

Concha Bielza, Pedro Larrañaga, Alfonso Ibáñez, 2011, Scientometrics.

Industrial Applications of Machine Learning

Concha Bielza, Pedro Larrañaga, David Atienza, 2018 .

A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices

Concha Bielza, Pedro Larrañaga, Irene Córdoba, 2018, IDEAL.

Tractable learning of Bayesian networks from partially observed data

Concha Bielza, Pedro Larrañaga, Marco Benjumeda, 2019, Pattern Recognit..

Interval-based ranking in noisy evolutionary multi-objective optimization

Concha Bielza, Pedro Larrañaga, Hossein Karshenas, 2014, Computational Optimization and Applications.

Medical Bayes Networks

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2000, ISMDA.

A partial orthogonalization method for simulating covariance and concentration graph matrices

Concha Bielza, Pedro Larrañaga, Irene Córdoba, 2018, PGM.

Learning Bayesian networks in the space of structures by estimation of distribution algorithms

Pedro Larrañaga, Iñaki Inza, Rosa Blanco, 2003, Int. J. Intell. Syst..

Adjusting Weights in Artificial Neural Networks using Evolutionary Algorithms

Pedro Larrañaga, Carlos Cotta, Ramón Sagarna, 2002, Estimation of Distribution Algorithms.

An Empirical Comparison Between K-Means, GAs and EDAs in Partitional Clustering

Pedro Larrañaga, Josep Roure Alcobé, Ramon Sangüesa, 2002, Estimation of Distribution Algorithms.

Combinatonal Optimization by Learning and Simulation of Bayesian Networks

Pedro Larrañaga, José Antonio Lozano, Ramon Etxeberria, 2000, UAI.

Hybrid Gaussian and von Mises Model-Based Clustering

Concha Bielza, Pedro Larrañaga, Sergio Luengo-Sanchez, 2016, ECAI.

Editorial Introduction Special Issue on Estimation of Distribution Algorithms

Pedro Larrañaga, José Antonio Lozano, 2005, Evolutionary Computation.

A regularity index for dendrites - local statistics of a neuron's input space

Concha Bielza, Pedro Larrañaga, Laura Anton-Sanchez, 2018, PLoS Comput. Biol..

Development of a Cyber-Physical System based on selective Gaussian naïve Bayes model for a self-predict laser surface heat treatment process control

Concha Bielza, Pedro Larrañaga, Javier Diaz, 2015, ML4CPS.

Regularized continuous estimation of distribution algorithms

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2013, Appl. Soft Comput..

Tree-Structured Bayesian Networks for Wrapped Cauchy Directional Distributions

Concha Bielza, Pedro Larrañaga, Ignacio Leguey, 2016, CAEPIA.

Directional naive Bayes classifiers

Concha Bielza, Pedro Larrañaga, Pedro L. López-Cruz, 2015, Pattern Analysis and Applications.

Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAs

Luis M. de Campos, Pedro Larrañaga, Serafín Moral, 2002, Estimation of Distribution Algorithms.

Partitional Cluster Analysis with Genetic Algorithms: Searching for the Number of Clusters

Pedro Larrañaga, Manuel Graña, Jose A. Lozano, 1998 .

Estimation of Distribution Algorithm (EDA)

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014 .

Synergies between evolutionary computation and probabilistic graphical models

Pedro Larrañaga, José Antonio Lozano, 2002, Int. J. Approx. Reason..

Dendritic and Axonal Wiring Optimization of Cortical GABAergic Interneurons

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2016, Neuroinformatics.

Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers

Concha Bielza, Pedro Larrañaga, João Gama, 2016, Intell. Data Anal..

A new measure for gene expression biclustering based on non-parametric correlation

Pedro Larrañaga, Iñaki Inza, Borja Calvo, 2013, Comput. Methods Programs Biomed..

Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers

Pedro Larrañaga, Iñaki Inza, Rubén Armañanzas, 2008, Comput. Methods Programs Biomed..

Bayesian Classifier (Naïve Bayes)

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014 .

Filter versus wrapper gene selection approaches in DNA microarray domains

Pedro Larrañaga, Iñaki Inza, Rosa Blanco, 2004, Artif. Intell. Medicine.

Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2005, Evolutionary Computation.

An empirical comparison of four initialization methods for the K-Means algorithm

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 1999, Pattern Recognit. Lett..

Mining Concept-Drifting Data Streams Containing Labeled and Unlabeled Instances

Concha Bielza, Pedro Larrañaga, Hanen Borchani, 2010, IEA/AIE.

Editorial

Pedro Larrañaga, Iñaki Inza, José Antonio Lozano, 2005, Machine Learning.

Machine Learning-based CPS for Clustering High throughput Machining Cycle Conditions

Concha Bielza, Pedro Larrañaga, Javier Diaz-Rozo, 2017 .

Synergies between Network-Based Representation and Probabilistic Graphical Models for Classification, Inference and Optimization Problems in Neuroscience

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2010, IEA/AIE.

Wrapper discretization by means of estimation of distribution algorithms

Pedro Larrañaga, Iñaki Inza, Jose Luis Flores, 2007, Intell. Data Anal..

A review of feature selection techniques in bioinformatics

Pedro Larrañaga, Yvan Saeys, Iñaki Inza, 2007, Bioinform..

Mathematical Modeling of Discrete Estimation of Distribution Algorithms

Pedro Larrañaga, Jose A. Lozano, Cristina González, 2002, Estimation of Distribution Algorithms.

Network measures for information extraction in evolutionary algorithms

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2013, Int. J. Comput. Intell. Syst..

Feature subset selection by genetic algorithms and estimation of distribution algorithms - A case study in the survival of cirrhotic patients treated with TIPS

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2001, Artif. Intell. Medicine.

Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices

Concha Bielza, Pedro Larrañaga, Alfonso Ibáñez, 2016, J. Assoc. Inf. Sci. Technol..

Genetic Algorithms Elitist Probabilistic of Degree 1, a generalization of Simulated Annealing

Pedro Larrañaga, Manuel Graña, Alicia D'Anjou, 1993, AI*IA.

Feature Subset Selection by Estimation of Distribution Algorithms

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2002, Estimation of Distribution Algorithms.

Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2014, IEEE Transactions on Evolutionary Computation.

Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation

Concha Bielza, Pedro Larrañaga, Pedro L. López-Cruz, 2014, Int. J. Approx. Reason..

Learning tractable Bayesian networks in the space of elimination orders

Concha Bielza, Pedro Larrañaga, Marco Benjumeda, 2019, Artif. Intell..

A review of estimation of distribution algorithms in bioinformatics

Concha Bielza, Pedro Larrañaga, Yvan Saeys, 2008, BioData Mining.

Regularized Multivariate von Mises Distribution

Concha Bielza, Pedro Larrañaga, Luis Rodriguez-Lujan, 2015, CAEPIA.

An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2000, Pattern Recognit. Lett..

What is behind a summary-evaluation decision?

Pedro Larrañaga, Ana Arruarte, Iraide Zipitria, 2008, Behavior research methods.

Bayesian Sparse Partial Least Squares

Concha Bielza, Pedro Larrañaga, Tom Heskes, 2013, Neural Computation.

Benefits of Data Clustering in Multimodal Function Optimization via EDAs

Pedro Larrañaga, Jose A. Lozano, Jose M. Peña, 2002, Estimation of Distribution Algorithms.

A review of undirected and acyclic directed Gaussian Markov model selection and estimation pdf

Irene Córdoba-Sánchez, Concha Bielza, Pedro Larrañaga, 2016, ArXiv.

Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms

Pedro Larrañaga, Cindy M. H. Kuijpers, Mikel Poza, 1997, Stat. Comput..

Classifying evolving data streams with partially labeled data

Concha Bielza, Pedro Larrañaga, Hanen Borchani, 2011, Intell. Data Anal..

On Applying Supervised Classification Techniques in Medicine

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2001, ISMDA.

Genetic Algorithms: Bridging the Convergence Gap

Pedro Larrañaga, Manuel Graña, José Antonio Lozano, 1999, Theor. Comput. Sci..

Architecture for anomaly detection in a laser heating surface process

Concha Bielza, Pedro Larrañaga, Javier Mesonero, 2017, 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA).

Ensemble of Classifiers

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014 .

Bayesian Classifiers in Optimization: An EDA-like Approach

Pedro Larrañaga, Endika Bengoetxea, Teresa Miquélez, 2006, Towards a New Evolutionary Computation.

Predicting the h-index with cost-sensitive naive Bayes

Concha Bielza, Pedro Larrañaga, Alfonso Ibáñez, 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

Wrapper positive Bayesian network classifiers

Pedro Larrañaga, Iñaki Inza, José Antonio Lozano, 2012, Knowledge and Information Systems.

MultiMap: A Tool to Automatically Extract and Analyse Spatial Microscopic Data From Large Stacks of Confocal Microscopy Images

Concha Bielza, Pedro Larrañaga, Gherardo Varando, 2018, Front. Neuroanat..

Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach

Concha Bielza, Pedro Larrañaga, Kallol Ray Chaudhuri, 2013, Artif. Intell. Medicine.

BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience

Concha Bielza, Pedro Larrañaga, Mario Michiels, 2020 .

Feature Subset Selection by Bayesian network-based optimization

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2000, Artif. Intell..

Markov Property in Generative Classifiers pdf

Concha Bielza, Pedro Larrañaga, Gherardo Varando, 2018, ArXiv.

Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks — A Case Study for the Optimal Ordering of Tables

Concha Bielza, Pedro Larrañaga, Juan A. Fernández del Pozo, 2013, Journal of Computer Science and Technology.

Using Probabilistic Dependencies Improves the Search of Conductance-Based Compartmental Neuron Models

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2010, EvoBIO.

GUEST EDITORIAL: Data mining in genomics and proteomics

Pedro Larrañaga, Víctor Robles, Ernestina Menasalvas, 2004 .

Multidimensional statistical analysis of the parameterization of a genetic algorithm for the optimal ordering of tables

Concha Bielza, Pedro Larrañaga, Endika Bengoetxea, 2010, Expert Syst. Appl..

Bayesian Networks, Rule Induction and Logistic Regression in the Prediction of the Survival of Women Suffering from Breast Cancer

Pedro Larrañaga, Basilio Sierra, Miren J. Gallego, 1997, EPIA.

Exact Bayesian network learning in estimation of distribution algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2007, 2007 IEEE Congress on Evolutionary Computation.

Classification of GABAergic interneurons by leading neuroscientists

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2019, Scientific Data.

Ensemble transcript interaction networks: A case study on Alzheimer's disease

Concha Bielza, Pedro Larrañaga, Rubén Armañanzas, 2012, Comput. Methods Programs Biomed..

Clustering of Data Streams With Dynamic Gaussian Mixture Models: An IoT Application in Industrial Processes

Concha Bielza, Pedro Larrañaga, Javier Diaz-Rozo, 2018, IEEE Internet of Things Journal.

Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters

Pedro Larrañaga, Yosu Yurramendi, Cindy M. H. Kuijpers, 1996, IEEE Trans. Pattern Anal. Mach. Intell..

The Role of a Priori Information in the Minimization of Contact Potentials by Means of Estimation of Distribution Algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2007, EvoBIO.

Semi-supervised Projected Clustering for Classifying GABAergic Interneurons

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2013, AIME.

Bayesian networks to answer challenging neuroscience questions

Concha Bielza, Pedro Larrañaga, 2013, CBMS.

Side chain placement using estimation of distribution algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2007, Artif. Intell. Medicine.

Analysis of the Univariate Marginal Distribution Algorithm Modeled by Markov Chains

Pedro Larrañaga, José Antonio Lozano, Cristina González, 2003, IWANN.

A Guide to the Literature on Inferring Genetic Networks by Probabilistic Graphical Models

Pedro Larrañaga, Iñaki Inza, Jose L. Flores, 2005, Data Analysis and Visualization in Genomics and Proteomics.

Selective Classifiers Can Be Too Restrictive: A Case-Study in Oesophageal Cancer

Pedro Larrañaga, Linda C. van der Gaag, Iñaki Inza, 2004, ISBMDA.

Applying genetic algorithms to search for the best hierarchical clustering of a dataset

Pedro Larrañaga, José Antonio Lozano, J. A. Lozano, 1999, Pattern Recognit. Lett..

Geographical clustering of cancer incidence by means of Bayesian networks and conditional Gaussian networks

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2001, AISTATS.

Asymmetric Hidden Markov Models with Continuous Variables

Concha Bielza, Pedro Larrañaga, Carlos Puerto-Santana, 2018, CAEPIA.

Multi-dimensional classification with Bayesian networks

Concha Bielza, Pedro Larrañaga, Guangdi Li, 2011, Int. J. Approx. Reason..

Combining variable neighborhood search and estimation of distribution algorithms in the protein side chain placement problem

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2007, J. Heuristics.

Models and Simulation of 3D Neuronal Dendritic Trees Using Bayesian Networks

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2011, Neuroinformatics.

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

Concha Bielza, Pedro Larrañaga, Miguel García-Torres, 2013, Inf. Sci..

Applications of Machine Learning in Industrial Sectors

Concha Bielza, Pedro Larrañaga, David Atienza, 2018, Industrial Applications of Machine Learning.

Discrete Bayesian Network Classifiers

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014, ACM Comput. Surv..

A Review on Estimation of Distribution Algorithms

Pedro Larrañaga, P. Larrañaga, 2002, Estimation of Distribution Algorithms.

The Convergence Behavior of the PBIL Algorithm

Pedro Larrañaga, Jose A. Lozano, Cristina González, 2001 .

Probabilistic graphical models in artificial intelligence

Pedro Larrañaga, Serafín Moral, P. Larrañaga, 2011, Appl. Soft Comput..

Circular Bayesian classifiers using wrapped Cauchy distributions

Concha Bielza, Pedro Larrañaga, Ignacio Leguey, 2019, Data Knowl. Eng..

Mateda-2.0: A MATLAB package for the implementation and analysis of estimation of distribution algorithms

Concha Bielza, Pedro Larrañaga, Jose A. Lozano, 2010 .

Guest Editorial: Special Issue on Evolutionary Algorithms Based on Probabilistic Models

Qingfu Zhang, Pedro Larrañaga, José Antonio Lozano, 2009, IEEE Trans. Evol. Comput..

Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductance-based neuron models

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2012, BMC Neuroscience.

An Introduction to Probabilistic Graphical Models

Pedro Larrañaga, P. Larrañaga, 2002, Estimation of Distribution Algorithms.

GA-EDA: hybrid evolutionary algorithm using genetic and estimation of distribution algorithms

Pedro Larrañaga, Víctor Robles, María S. Pérez, 2004 .

Discrete model-based clustering with overlapping subsets of attributes

Concha Bielza, Pedro Larrañaga, Fernando Rodriguez-Sanchez, 2018, PGM.

Learning Semi Naïve Bayes Structures by Estimation of Distribution Algorithms

María S. Pérez-Hernández, Pedro Larrañaga, Ernestina Menasalvas Ruiz, 2003, EPIA.

Feature Weighting for Nearest Neighbor by Estimation of Distribution Algorithms

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2002, Estimation of Distribution Algorithms.

Learning Bayesisan Networks by Genetic Algorithms: A Case Study in the Prediction of Survival in Malignant Skin Melanoma

Pedro Larrañaga, Basilio Sierra, Juan Manuel Pikatza Atxa, 1997, AIME.

Towards optimal neuronal wiring through estimation of distribution algorithms

Concha Bielza, Pedro Larrañaga, Laura Anton-Sanchez, 2013, GECCO.

On nonlinearity in neural encoding models applied to the primary visual cortex

Concha Bielza, Pedro Larrañaga, Diego Vidaurre, 2011, Network.

Predicting citation count of Bioinformatics papers within four years of publication

Concha Bielza, Pedro Larrañaga, Alfonso Ibáñez, 2009, Bioinform..

The von Mises Naive Bayes Classifier for Angular Data

Concha Bielza, Pedro Larrañaga, Pedro L. López-Cruz, 2011, CAEPIA.

Information Theory and Classification Error in Probabilistic Classifiers

Pedro Larrañaga, Iñaki Inza, Aritz Pérez Martínez, 2006, Discovery Science.

Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes

Pedro Larrañaga, Iñaki Inza, Aritz Pérez Martínez, 2006, Int. J. Approx. Reason..

Optimization in Continuous Domains by Learning and Simulation of Gaussian Networks

Pedro Larrañaga, Jose A. Lozano, Jose M. Peña, 2000 .

Performance evaluation of compromise conditional Gaussian networks for data clustering

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2001, Int. J. Approx. Reason..

3D morphology-based clustering and simulation of human pyramidal cell dendritic spines

Concha Bielza, Pedro Larrañaga, Sergio Luengo-Sanchez, 2018, PLoS Comput. Biol..

EDA-PSO: A Hybrid Paradigm Combining Estimation of Distribution Algorithms and Particle Swarm Optimization

Pedro Larrañaga, Endika Bengoetxea, P. Larrañaga, 2010, ANTS Conference.

Akaike Information Criterion

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014 .

Learning Mixtures of Polynomials of Conditional Densities from Data

Concha Bielza, Pedro Larrañaga, Thomas D. Nielsen, 2013, CAEPIA.

Article in Press G Model Artificial Intelligence in Medicine Classifying Gabaergic Interneurons with Semi-supervised Projected Model-based Clustering

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2022 .

Optimizing Brain Networks Topologies Using Multi-objective Evolutionary Computation

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2011, Neuroinformatics.

GA-EDA: Hybrid Evolutionary Algorithm Using Genetic and Estimation of Distribution Algorithms

María S. Pérez-Hernández, Pedro Larrañaga, Francisco Rosales, 2004, IEA/AIE.

Machine-Level Case Study: Fingerprint of Industrial Motors

Concha Bielza, Pedro Larrañaga, David Atienza, 2018, Industrial Applications of Machine Learning.

Genetic algorithms applied to Bayesian networks

Pedro Larrañaga, Jose A. Lozano, Manuel Graña, 1996 .

Learning Bayesian Networks In The Space Of Orderings With Estimation Of Distribution Algorithms

Pedro Larrañaga, Basilio Sierra, Txomin Romero, 2004, Int. J. Pattern Recognit. Artif. Intell..

Continuous Estimation of Distribution Algorithms Based on Factorized Gaussian Markov Networks

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2012 .

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2014, Front. Comput. Neurosci..

BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience

Concha Bielza, Pedro Larrañaga, Mario Michiels, 2020, bioRxiv.

Decision functions for chain classifiers based on Bayesian networks for multi-label classification

Concha Bielza, Pedro Larrañaga, Gherardo Varando, 2016, Int. J. Approx. Reason..

A comparison of clustering quality indices using outliers and noise

Concha Bielza, Pedro Larrañaga, Víctor Robles, 2012, Intell. Data Anal..

Quantitative genetics in multi-objective optimization algorithms: from useful insights to effective methods

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2011, GECCO '11.

Incremental Learning of Latent Forests

Concha Bielza, Pedro Larrañaga, Fernando Rodriguez-Sanchez, 2020, IEEE Access.

Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers

Concha Bielza, Pedro Larrañaga, Hanen Borchani, 2013, Artif. Intell. Medicine.

Selection of human embryos for transfer by Bayesian classifiers

Pedro Larrañaga, Endika Bengoetxea, Dinora Araceli Morales, 2008, Comput. Biol. Medicine.

Component weighting functions for adaptive search with EDAs

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

Bayesian Network Classifiers for Categorizing Cortical GABAergic Interneurons

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2015, Neuroinformatics.

Decision boundary for discrete Bayesian network classifiers

Concha Bielza, Pedro Larrañaga, Gherardo Varando, 2015, J. Mach. Learn. Res..

Anomaly Detection with a Spatio-Temporal Tracking of the Laser Spot

Concha Bielza, Pedro Larrañaga, David Atienza, 2016, STAIRS.

Univariate and bivariate truncated von Mises distributions

Concha Bielza, Pedro Larrañaga, Pablo Fernandez-Gonzalez, 2017, Progress in Artificial Intelligence.

Solving the 0-1 Knapsack Problem with EDAs

Pedro Larrañaga, Ramón Sagarna, 2002, Estimation of Distribution Algorithms.

Regularized logistic regression and multiobjective variable selection for classifying MEG data

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2012, Biological Cybernetics.

Probabilistic Graphical Markov Model Learning: An Adaptive Strategy

Concha Bielza, Pedro Larrañaga, Eunice Ponce de León, 2009, MICAI.

Solving Graph Matching with EDAs Using a Permutation-Based Representation

Isabelle Bloch, Pedro Larrañaga, Endika Bengoetxea, 2002, Estimation of Distribution Algorithms.

Mining probabilistic models learned by EDAs in the optimization of multi-objective problems

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2009, GECCO.

Inexact graph matching for model-based recognition: Evaluation and comparison of optimization algorithms

Roberto Marcondes Cesar Junior, Isabelle Bloch, Pedro Larrañaga, 2005, Pattern Recognit..

Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2002, Machine Learning.

Estimation of Distribution Algorithms for Feature Subset Selection in Large Dimensionality Domains

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2002 .

Learning Bayesian classifiers from positive and unlabeled examples

Pedro Larrañaga, José Antonio Lozano, Borja Calvo, 2007, Pattern Recognit. Lett..

Machine learning: an indispensable tool in bioinformatics.

Pedro Larrañaga, Iñaki Inza, Endika Bengoetxea, 2010, Methods in molecular biology.

Estimation of Distribution Algorithms Applied To Combinatorial Optimization Problems

Pedro Larrañaga, Jose A. Lozano, H. Mühlenbein, 2003 .

Structure of the high-order Boltzmann machine from independence maps

Pedro Larrañaga, Manuel Graña, F. Xabier Albizuri, 1997, IEEE Trans. Neural Networks.

Estimation of Distribution Algorithms

Pedro Larrañaga, Jose A. Lozano, J. A. Lozano, 2002, Genetic Algorithms and Evolutionary Computation.

Learning Bayesian networks for clustering by means of constructive induction

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 1999, Pattern Recognit. Lett..

Bayesian classifiers based on kernel density estimation: Flexible classifiers

Pedro Larrañaga, Iñaki Inza, Aritz Pérez Martínez, 2009, Int. J. Approx. Reason..

A partially supervised classification approach to dominant and recessive human disease gene prediction

Pedro Larrañaga, Borja Calvo, Núria López-Bigas, 2007, Comput. Methods Programs Biomed..

Feature subset selection from positive and unlabelled examples

Pedro Larrañaga, José Antonio Lozano, Borja Calvo, 2009, Pattern Recognit. Lett..

Discriminative vs. Generative Learning of Bayesian Network Classifiers

Pedro Larrañaga, José Antonio Lozano, Guzmán Santafé, 2007, ECSQARU.

Bayesian Chain Classifiers for Multidimensional Classification

Concha Bielza, Pedro Larrañaga, Luis Enrique Sucar, 2011, IJCAI.

Machine Learning : Editorial

Pedro Larrañaga, Jose A. Lozano, Iñaki Inza, 2005 .

Floating Search Methods in Learning Bayesian Networks

Pedro Larrañaga, Iñaki Inza, Rosa Blanco, 2002, Probabilistic Graphical Models.

GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm

María S. Pérez-Hernández, Pedro Larrañaga, José M. Peña, 2006, Towards a New Evolutionary Computation.

bnclassify: Learning Bayesian Network Classifiers

Concha Bielza, Pedro Larrañaga, Bojan Mihaljević, 2018, R J..

Inference of Population Structure Using Genetic Markers and a Bayesian Model Averaging Approach for Clustering

Pedro Larrañaga, José Antonio Lozano, Guzmán Santafé, 2008, J. Comput. Biol..

Analysis of the behaviour of genetic algorithms when learning Bayesian network structure from data

Pedro Larrañaga, Ramon Etxeberria, Juan Manuel Pikatza Atxa, 1997, Pattern Recognit. Lett..

A review on probabilistic graphical models in evolutionary computation

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2012, Journal of Heuristics.

International Journal of Approximate Reasoning Tractability of most probable explanations in multidimensional Bayesian network classiﬁers ✩

Concha Bielza, Pedro Larrañaga, Marco Benjumeda, 2022 .

Multi-objective Optimization with Joint Probabilistic Modeling of Objectives and Variables

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2011, EMO.

Learning Tractable Multidimensional Bayesian Network Classifiers

Concha Bielza, Pedro Larrañaga, Marco Benjumeda, 2016, Probabilistic Graphical Models.

Mixtures of Kikuchi Approximations

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2006, ECML.

Inexact graph matching by means of estimation of distribution algorithms

Isabelle Bloch, Pedro Larrañaga, Endika Bengoetxea, 2002, Pattern Recognit..

Independent Component Analysis (ICA)

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014 .

Parallel Estimation of Distribution Algorithms

Pedro Larrañaga, Jose A. Lozano, Ramón Sagarna, 2002, Estimation of Distribution Algorithms.

Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms

Pedro Larrañaga, Cindy M. H. Kuijpers, Mikel Poza, 1995, AISTATS.

Mateda-2.0: Estimation of Distribution Algorithms in MATLAB

Concha Bielza, Pedro Larrañaga, José Antonio Lozano Alonso, 2010 .

An L1-Regularized naïVE Bayes-Inspired Classifier for Discarding Redundant and Irrelevant Predictors

Concha Bielza, Pedro Larrañaga, Diego Vidaurre, 2013, Int. J. Artif. Intell. Tools.

Parallel Stochastic Search for Protein Secondary Structure Prediction

María S. Pérez-Hernández, Pedro Larrañaga, Víctor Robles, 2003, PPAM.

Improvement of naive Bayes collaborative filtering using interval estimation

María S. Pérez-Hernández, Pedro Larrañaga, Ernestina Menasalvas Ruiz, 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003).

Evolutionary Bayesian Classifier-Based Optimization in Continuous Domains

Pedro Larrañaga, Endika Bengoetxea, Teresa Miquélez, 2006, SEAL.

Solving the Traveling Salesman Problem with EDAs

Pedro Larrañaga, Víctor Robles, P. de Miguel, 2002, Estimation of Distribution Algorithms.

Protein Folding in 2-Dimensional Lattices with Estimation of Distribution Algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2004, ISBMDA.

Evolutionary computation based on Bayesian classifiers

Pedro Larrañaga, Endika Bengoetxea, Teresa Miquélez, 2004 .

Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS

Pedro Larrañaga, Iñaki Inza, Rosa Blanco, 2005, J. Biomed. Informatics.

Three-dimensional distribution of cortical synapses: a replicated point pattern-based analysis

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2014, Front. Neuroanat..

Average Time Complexity of Estimation of Distribution Algorithms

Pedro Larrañaga, José Antonio Lozano, Cristina González, 2005, IWANN.

Data-Driven Computational Neuroscience

Concha Bielza, Pedro Larrañaga, C. Bielza, 2020 .

Protein Folding in Simplified Models With Estimation of Distribution Algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2008, IEEE Transactions on Evolutionary Computation.

Stratified Cross-Validation in Multi-Label Classification Using Genetic Algorithms

Concha Bielza, Pedro Larrañaga, C. Bielza, 2013 .

Best-First Search

Concha Bielza, Pedro Larrañaga, 2014 .

Regularized k-order markov models in EDAs

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2011, GECCO '11.

Combining Bayesian classifiers and estimation of distribution algorithms for optimization in continuous domains

Pedro Larrañaga, Endika Bengoetxea, Alexander Mendiburu, 2007, Connect. Sci..

Bayesian Model Averaging of TAN Models for Clustering

Pedro Larrañaga, José Antonio Lozano, Guzmán Santafé, 2006, Probabilistic Graphical Models.

Structure Learning of Bayesian Networks by Genetic Algorithms

Pedro Larrañaga, Mikel Poza, 1994 .

Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms

José Ignacio Hidalgo, Concha Bielza, Pedro Larrañaga, 2010, IEEE Congress on Evolutionary Computation.

Discriminative Learning of Bayesian Network Classifiers

Pedro Larrañaga, José Antonio Lozano, Guzmán Santafé, 2006, Inteligencia Artif..

Sparse regularized local regression

Concha Bielza, Pedro Larrañaga, Diego Vidaurre, 2013, Comput. Stat. Data Anal..

Affinity propagation enhanced by estimation of distribution algorithms

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2011, GECCO '11.

Adding Probabilistic Dependencies to the Search of Protein Side Chain Configurations Using EDAs

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2008, PPSN.

Regularized logistic regression without a penalty term: An application to cancer classification with microarray data

Concha Bielza, Pedro Larrañaga, Víctor Robles, 2011, Expert Syst. Appl..

Experimental Results of a Michigan-like Evolution Strategy for Non-stationary Clustering

Pedro Larrañaga, Manuel Graña, Jose A. Lozano, 1997, ICANNGA.

Mathematical modelling of UMDAc algorithm with tournament selection. Behaviour on linear and quadratic functions

Pedro Larrañaga, José Antonio Lozano, Cristina González, 2002, Int. J. Approx. Reason..

Dynamic Bayesian Network-Based Anomaly Detection for In-Process Visual Inspection of Laser Surface Heat Treatment

Concha Bielza, Pedro Larrañaga, Javier Diaz-Rozo, 2016, ML4CPS.

Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators

Pedro Larrañaga, Iñaki Inza, Cindy M. H. Kuijpers, 1999, Artificial Intelligence Review.

Research topics in discrete estimation of distribution algorithms based on factorizations

Pedro Larrañaga, Jose A. Lozano, Roberto Santana, 2009, Memetic Comput..

Learning Bayesian networks with low inference complexity

Concha Bielza, Pedro Larrañaga, Marco Benjumeda, 2016, Progress in Artificial Intelligence.

Rule Induction by Estimation of Distribution Algorithms

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2002, Estimation of Distribution Algorithms.

Learning Bayesian network classifiers with completed partially directed acyclic graphs

Concha Bielza, Pedro Larrañaga, Bojan Mihaljevic, 2018, PGM.

A survey on multi‐output regression

Concha Bielza, Pedro Larrañaga, Gherardo Varando, 2015, WIREs Data Mining Knowl. Discov..

Machine Learning Inspired Approaches to Combine Standard Medical Measures at an Intensive Care Unit

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 1999, AIMDM.

Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches

Pedro Larrañaga, Basilio Sierra, P. Larrañaga, 1998, Artif. Intell. Medicine.

A univocal definition of the neuronal soma morphology using Gaussian mixture models

Concha Bielza, Pedro Larrañaga, Sergio Luengo-Sanchez, 2015, Front. Neuroanat..

Triangulation of Bayesian networks with recursive estimation of distribution algorithms

Pedro Larrañaga, Txomin Romero, P. Larrañaga, 2009, Int. J. Approx. Reason..

Affinity Propagation-based Clustering

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014 .

Classification of neural signals from sparse autoregressive features

Concha Bielza, Pedro Larrañaga, Diego Vidaurre, 2013, Neurocomputing.

Maximizing the number of polychronous groups in spiking networks

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2012, GECCO '12.

Bayesian networks in neuroscience: a survey

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014, Front. Comput. Neurosci..

A review of Gaussian Markov models for conditional independence

Concha Bielza, Pedro Larrañaga, Irene Córdoba, 2019 .

Discriminative Learning of Bayesian Network Classifiers via the TM Algorithm

Pedro Larrañaga, José Antonio Lozano, Guzmán Santafé, 2005, ECSQARU.

Multi-label classification with Bayesian network-based chain classifiers

Concha Bielza, Pedro Larrañaga, Pablo Hernandez-Leal, 2014, Pattern Recognit. Lett..

Validation Measures for Clustering

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014 .

Augmented Semi-naive Bayes Classifier

Concha Bielza, Pedro Larrañaga, Bojan Mihaljevic, 2013, CAEPIA.

The Impact of Exact Probabilistic Learning Algorithms in EDAs Based on Bayesian Networks

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2008, Linkage in Evolutionary Computation.

A Directional-Linear Bayesian Network and Its Application for Clustering and Simulation of Neural Somas

Concha Bielza, Pedro Larrañaga, Sergio Luengo-Sanchez, 2019, IEEE Access.

Experimental Results in Function Optimization with EDAs in Continuous Domain

Pedro Larrañaga, Jose A. Lozano, Endika Bengoetxea, 2002, Estimation of Distribution Algorithms.

Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks

Pedro Larrañaga, Iñaki Inza, José Antonio Lozano, 2001, IEEE Trans. Pattern Anal. Mach. Intell..

Machine Learning in Bioinformatics

Concha Bielza, Pedro Larrañaga, Iñaki Inza, 2008, Encyclopedia of Database Systems.

Towards a supervised classification of neocortical interneuron morphologies

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2018, BMC Bioinformatics.

Algoritmos de Estimación de Distribuciones en Problemas de Optimización Combinatoria

Pedro Larrañaga, Heinz Mühlenbein, José Antonio Lozano, 2003, Inteligencia Artif..

Unsupervised Learning Of Bayesian Networks Via Estimation Of Distribution Algorithms: An Application To Gene Expression Data Clustering

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2004, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

Learning Bayesian Networks by Floating Search Methods

Pedro Larrañaga, Rosa Blanco, Iiiaki Inza, 2004 .

Analyzing the Population Based Incremental Learning Algorithm by Means of Discrete Dynamical Systems

Pedro Larrañaga, José Antonio Lozano, Cristina González, 2000, Complex Syst..

Forward stagewise naïve Bayes

Concha Bielza, Pedro Larrañaga, Diego Vidaurre, 2011, Progress in Artificial Intelligence.

Frobenius Norm Regularization for the Multivariate Von Mises Distribution

Concha Bielza, Pedro Larrañaga, Luis Rodriguez-Lujan, 2017, Int. J. Intell. Syst..

Bayesian Classifiers with Consensus Gene Selection: A Case Study in the Systemic Lupus Erythematosus

Pedro Larrañaga, Iñaki Inza, Borja Calvo, 2008 .

Finite Mixture Model

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014 .

Relationship among research collaboration, number of documents and number of citations: a case study in Spanish computer science production in 2000–2009

Concha Bielza, Pedro Larrañaga, Alfonso Ibáñez, 2013, Scientometrics.

Towards Gaussian Bayesian Network Fusion

Irene Córdoba-Sánchez, Concha Bielza, Pedro Larrañaga, 2015, ECSQARU.

Moral Graph, Triangulation of

Pedro Larrañaga, C.M.H. Kuipers, P. Larrañaga, 2006 .

Lazy lasso for local regression

Concha Bielza, Pedro Larrañaga, Diego Vidaurre, 2012, Comput. Stat..

Learning Bayesian network structures by searching for the best ordering with genetic algorithms

Pedro Larrañaga, Yosu Yurramendi, Cindy M. H. Kuijpers, 1996, IEEE Trans. Syst. Man Cybern. Part A.

Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2001, Int. J. Approx. Reason..

A review on evolutionary algorithms in Bayesian network learning and inference tasks

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2013, Inf. Sci..

Interactions and dependencies in estimation of distribution algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2005, 2005 IEEE Congress on Evolutionary Computation.

Feature Subset Selection Using Probabilistic Tree Structures. A Case Study in the Survival of Cirrhotic Patients Treated with TIPS

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2000, ISMDA.

Data Publications Correlate with Citation Impact

Concha Bielza, Pedro Larrañaga, Florian Leitner, 2016, Front. Neurosci..

Learning Factorizations in Estimation of Distribution Algorithms Using Affinity Propagation

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2010, Evolutionary Computation.

Multi-Dimensional Classification with Super-Classes

Concha Bielza, Pedro Larrañaga, Jesse Read, 2014, IEEE Transactions on Knowledge and Data Engineering.

Estimation of distribution algorithms with affinity propagation methods

Pedro Larrañaga, Roberto Santana, Jose Antonio Lozano, 2008 .

A circular-linear dependence measure under Johnson-Wehrly distributions and its application in Bayesian networks

Concha Bielza, Pedro Larrañaga, Shogo Kato, 2019, Inf. Sci..

Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2001, Artif. Intell. Medicine.

Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering

Concha Bielza, Pedro Larrañaga, Javier Diaz-Rozo, 2020, Eng. Appl. Artif. Intell..

Cluster methods for assessing research performance: exploring Spanish computer science

Concha Bielza, Pedro Larrañaga, Alfonso Ibáñez, 2013, Scientometrics.

Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification

Concha Bielza, Pedro Larrañaga, Gherardo Varando, 2014, Probabilistic Graphical Models.

Learning Conditional Linear Gaussian Classifiers with Probabilistic Class Labels

Concha Bielza, Pedro Larrañaga, Pedro L. López-Cruz, 2013, CAEPIA.

Microarray Analysis of Autoimmune Diseases by Machine Learning Procedures

Pedro Larrañaga, Iñaki Inza, Borja Calvo, 2009, IEEE Transactions on Information Technology in Biomedicine.

Protein structure prediction in simplified models with estimation of distribution algorithms

Pedro Larrañaga, Jose A. Lozano, Roberto Santana, 2005 .

Interval Estimation Naïve Bayes

María S. Pérez-Hernández, Pedro Larrañaga, Ernestina Menasalvas Ruiz, 2003, IDA.

Random Forests for Regression as a Weighted Sum of ${k}$ -Potential Nearest Neighbors

Pedro Larrañaga, Pablo Fernández-González, Concepción Bielza, 2019, IEEE Access.

Conditional Density Approximations with Mixtures of Polynomials

Concha Bielza, Pedro Larrañaga, Gherardo Varando, 2015, Int. J. Intell. Syst..

Adaptive Estimation of Distribution Algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2008, Adaptive and Multilevel Metaheuristics.

Aplicación de los algoritmos genéticos al problema del clustering jerárquico

Pedro Larrañaga, José Antonio Lozano, J. Lozano, 1998, Inteligencia Artif..

Cost-sensitive selective naive Bayes classifiers for predicting the increase of the h-index for scientific journals

Concha Bielza, Pedro Larrañaga, Alfonso Ibáñez, 2014, Neurocomputing.

Bayesian network modeling of the consensus between experts: An application to neuron classification

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2014 .

Bayesian network multi-classifiers for protein secondary structure prediction

María S. Pérez-Hernández, Pedro Larrañaga, Ernestina Menasalvas Ruiz, 2004, Artif. Intell. Medicine.

Prototype Selection and Feature Subset Selection by Estimation of Distribution Algorithms. A Case Study in the Survival of Cirrhotic Patients Treated with TIPS

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2001, AIME.

Component-Level Case Study: Remaining Useful Life of Bearings

Concha Bielza, Pedro Larrañaga, David Atienza, 2018, Industrial Applications of Machine Learning.

Evolutionary strategies for adaptive color quantization

Pedro Larrañaga, José Antonio Lozano Alonso, Manuel Graña Romay, 1996 .

Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms

Concha Bielza, Pedro Larrañaga, Yvan Saeys, 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Network design through forests with degree- and role-constrained minimum spanning trees

Concha Bielza, Pedro Larrañaga, Laura Anton-Sanchez, 2017, J. Heuristics.

Learning an L1-Regularized Gaussian Bayesian Network in the Equivalence Class Space

Concha Bielza, Pedro Larrañaga, Diego Vidaurre, 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

Feature Subset Selection

Concha Bielza, Pedro Larrañaga, 2014 .

Semi-supervised projected model-based clustering

Concha Bielza, Pedro Larrañaga, Víctor Robles, 2014, Data Mining and Knowledge Discovery.

Estimation of Distribution Algorithms: A New Evolutionary Computation Approach for Graph Matching Problems

Isabelle Bloch, Pedro Larrañaga, Endika Bengoetxea, 2001, EMMCVPR.

Multidimensional classifiers for neuroanatomical data

Concha Bielza, Pedro Larrañaga, Pablo Fernandez-Gonzalez, 2015, ICML 2015.

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks

Concha Bielza, Pedro Larrañaga, Mario Michiels, 2020, bioRxiv.

Collaborative Filtering Using Interval Estimation Naïve Bayes

María S. Pérez-Hernández, Pedro Larrañaga, José M. Peña, 2003, AWIC.

Bayesian classification for the selection of in vitro human embryos using morphological and clinical data

Pedro Larrañaga, Endika Bengoetxea, Miguel García, 2008, Comput. Methods Programs Biomed..

Multi-dimensional Bayesian Network Classifier Trees

Concha Bielza, Pedro Larrañaga, Santiago Gil-Begue, 2018, IDEAL.

Production-Level Case Study: Automated Visual Inspection of a Laser Process

Concha Bielza, Pedro Larrañaga, David Atienza, 2018, Industrial Applications of Machine Learning.

DISCOVER WILEY'S LEADING STATISTICS JOURNALS

Concha Bielza, Pedro Larrañaga, Serge Rudaz, 2014 .

Optimal row and column ordering to improve table interpretation using estimation of distribution algorithms

Concha Bielza, Pedro Larrañaga, Endika Bengoetxea, 2011, J. Heuristics.

Long-term forecasting of multivariate time series in industrial furnaces with dynamic Gaussian Bayesian networks

Concha Bielza, Pedro Larrañaga, David Quesada, 2021, Eng. Appl. Artif. Intell..

Structure Learning of High-Order Dynamic Bayesian Networks via Particle Swarm Optimization with Order Invariant Encoding

Concha Bielza, Pedro Larrañaga, David Quesada, 2021, HAIS.

Multidimensional continuous time Bayesian network classifiers

Concha Bielza, Pedro Larrañaga, Carlos Villa-Blanco, 2021, Int. J. Intell. Syst..

Comparing the Electrophysiology and Morphology of Human and Mouse Layer 2/3 Pyramidal Neurons With Bayesian Networks

Concha Bielza, Pedro Larrañaga, Bojan Mihaljević, 2021, Frontiers in Neuroinformatics.

Quantum-Inspired Estimation Of Distribution Algorithm To Solve The Travelling Salesman Problem

Concha Bielza, Pedro Larrañaga, Vicente P. Soloviev, 2021, 2021 IEEE Congress on Evolutionary Computation (CEC).

An Online Feature Selection Methodology for Ball-Bearing Harmonic Frequencies Based on HMMs

Concha Bielza, Pedro Larrañaga, Javier Diaz-Rozo, 2021, SOCO.

Bayesian networks for interpretable machine learning and optimization

Concha Bielza, Pedro Larrañaga, Bojan Mihaljevic, 2021, Neurocomputing.

Automatic segmentation of zona pellucida in HMC images of human embryos

Pedro Larrañaga, Endika Bengoetxea, A. Heyden, 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

New insights into the classification and nomenclature of cortical GABAergic interneurons

Concha Bielza, Pedro Larrañaga, Rafael Yuste, 2013, Nature Reviews Neuroscience.

Identification of a biomarker panel for colorectal cancer diagnosis

Pedro Larrañaga, Iñaki Inza, Rubén Armañanzas, 2012, BMC Cancer.

A Survey of L1 Regression

Concha Bielza, Pedro Larrañaga, Diego Vidaurre, 2013 .

Three-Dimensional Spatial Distribution of Synapses in the Neocortex: A Dual-Beam Electron Microscopy Study

Concha Bielza, Pedro Larrañaga, Víctor Robles, 2013, Cerebral cortex.

Gaussian-Stacking Multiclassifiers for Human Embryo Selection

Pedro Larrañaga, Endika Bengoetxea, P. Larrañaga, 2009 .

Laminar Differences in Dendritic Structure of Pyramidal Neurons in the Juvenile Rat Somatosensory Cortex

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2016, Cerebral cortex.

Learning Bayesian networks from data . Some applications in biomedicine

Pedro Larrañaga, P. Larrañaga, 2002 .

Learning mixtures of polynomials from data using B-spline interpolation

Concha Bielza, Pedro Larrañaga, Pedro L. López-Cruz, 2012 .

Optimization by Max-Propagation Using Kikuchi Approximations

Pedro Larrañaga, Robin Höns, Jose A. Lozano, 2007 .

Dendritic branching angles of pyramidal cells across layers of the juvenile rat somatosensory cortex

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2016, The Journal of comparative neurology.

The Convergence Behavior of the PBIL Algorithm: A Preliminary Approach

Pedro Larrañaga, Jose A. Lozano, J. A. Lozano, 2001 .

Branching angles of pyramidal cell dendrites follow common geometrical design principles in different cortical areas

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2014, Scientific Reports.