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- affiliation: Universidad Politécnica de Madrid
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2020 – today
- 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)- [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) - [c52]Vicente P. Soloviev, Pedro Larrañaga, Concha Bielza:
Quantum parametric circuit optimization with estimation of distribution algorithms. GECCO Companion 2022: 2247-2250 - [c51]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 - [c50]Enrique Valero-Leal, Pedro Larrañaga, Concha Bielza:
Interpreting Time-Varying Dynamic Bayesian Networks for Earth Climate Modelling. PGM 2022: 373-384 - [c49]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) - [c48]Vicente P. Soloviev, Concha Bielza, Pedro Larrañaga:
Quantum-Inspired Estimation Of Distribution Algorithm To Solve The Travelling Salesman Problem. CEC 2021: 416-425 - [c47]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 - [c46]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) - [c45]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)
2010 – 2019
- 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) - [c44]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 - [c43]Carlos Puerto-Santana, Concha Bielza, Pedro Larrañaga:
Asymmetric Hidden Markov Models with Continuous Variables. CAEPIA 2018: 98-107 - [c42]Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga:
A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices. IDEAL (1) 2018: 117-124 - [c41]Santiago Gil-Begue, Pedro Larrañaga, Concha Bielza:
Multi-dimensional Bayesian Network Classifier Trees. IDEAL (1) 2018: 354-363 - [c40]Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga:
A partial orthogonalization method for simulating covariance and concentration graph matrices. PGM 2018: 61-72 - [c39]Bojan Mihaljevic, Concha Bielza, Pedro Larrañaga:
Learning Bayesian network classifiers with completed partially directed acyclic graphs. PGM 2018: 272-283 - [c38]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) - [c37]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) - [c36]Ignacio Leguey, Concha Bielza, Pedro Larrañaga:
Tree-Structured Bayesian Networks for Wrapped Cauchy Directional Distributions. CAEPIA 2016: 207-216 - [c35]Sergio Luengo-Sanchez, Concha Bielza, Pedro Larrañaga:
Hybrid Gaussian and von Mises Model-Based Clustering. ECAI 2016: 855-862 - [c34]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 - [c33]Marco Benjumeda, Concha Bielza, Pedro Larrañaga:
Learning Tractable Multidimensional Bayesian Network Classifiers. Probabilistic Graphical Models 2016: 13-24 - [c32]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) - [j60]Concha Bielza, João Gama, Alípio Jorge, Indre Zliobaite:
Guest editors introduction: special issue of the ECMLPKDD 2015 journal track. Data Min. Knowl. Discov. 29(5): 1113-1115 (2015) - [j59]Concha Bielza, Serafín Moral, Antonio Salmerón:
Recent Advances in Probabilistic Graphical Models. Int. J. Intell. Syst. 30(3): 207-208 (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) - [j56]Concha Bielza, João Gama, Alípio Jorge, Indre Zliobaite:
Guest Editors introduction: special issue of the ECMLPKDD 2015 journal track. Mach. Learn. 100(2-3): 157-159 (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) - [c31]Luis Rodriguez-Lujan, Concha Bielza, Pedro Larrañaga:
Regularized Multivariate von Mises Distribution. CAEPIA 2015: 25-35 - [c30]Irene Córdoba-Sánchez, Concha Bielza, Pedro Larrañaga:
Towards Gaussian Bayesian Network Fusion. ECSQARU 2015: 519-528 - [c29]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) - [c28]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) - [c27]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 - [c26]Pedro L. López-Cruz, Concha Bielza, Pedro Larrañaga:
Learning Conditional Linear Gaussian Classifiers with Probabilistic Class Labels. CAEPIA 2013: 139-148 - [c25]Bojan Mihaljevic, Pedro Larrañaga, Concha Bielza:
Augmented Semi-naive Bayes Classifier. CAEPIA 2013: 159-167 - [c24]