Andrés Ramón Masegosa Arredondo-rekin lankidetzan egindako argitalpenak (14)

2018

  1. Virtual subconcept drift detection in discrete data using probabilistic graphical models

    Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications: 17th International Conference, IPMU 2018, Cádiz, Spain, June 11-15, 2018, Proceedings, Part III

  2. Virtual subconcept drift detection in discrete data using probabilistic graphical models

    Communications in Computer and Information Science

2011

  1. A memory efficient semi-Naive Bayes classifier with grouping of cases

    Intelligent Data Analysis, Vol. 15, Núm. 3, pp. 299-318

  2. A method for integrating expert knowledge when learning bayesian networks from data

    IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 41, Núm. 5, pp. 1382-1394

  3. Comparing binary and standard probability trees in credal networks inference

    ISIPTA 2011 - Proceedings of the 7th International Symposium on Imprecise Probability: Theories and Applications

  4. Learning with Bayesian networks and probability trees to approximate a joint distribution

    International Conference on Intelligent Systems Design and Applications, ISDA

  5. Locally averaged bayesian dirichlet metrics

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2010

  1. An importance sampling approach to integrate expert knowledge when learning Bayesian networks from data

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2009

  1. A Bayesian random split to build ensembles of classification trees

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2008

  1. A Bayesian approach to estimate probabilities in classification trees

    Proceedings of the 4th European Workshop on Probabilistic Graphical Models, PGM 2008

2007

  1. A semi-naive bayes classifier with grouping of cases

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2005

  1. Methods to determine the branching attribute in Bayesian multinets classifiers

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  2. Selective gaussian naïve bayes model for Diffuse Large-B-Cell Lymphoma classification: Some improvements in preprocessing and variable elimination

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)