Publicaciones en las que colabora con Jerónimo Hernández González (13)

2019

  1. A Note on the Behavior of Majority Voting in Multi-Class Domains with Biased Annotators

    IEEE Transactions on Knowledge and Data Engineering, Vol. 31, Núm. 1, pp. 195-200

  2. Aggregated outputs by linear models: An application on marine litter beaching prediction

    Information Sciences, Vol. 481, pp. 381-393

2018

  1. Fitting the data from embryo implantation prediction: Learning from label proportions

    Statistical Methods in Medical Research, Vol. 27, Núm. 4, pp. 1056-1066

  2. Learning to classify software defects from crowds: A novel approach

    Applied Soft Computing Journal, Vol. 62, pp. 579-591

  3. Two datasets of defect reports labeled by a crowd of annotators of unknown reliability

    Data in Brief

2017

  1. Learning from Proportions of Positive and Unlabeled Examples

    International Journal of Intelligent Systems, Vol. 32, Núm. 2, pp. 109-133

2016

  1. Weak supervision and other non-standard classification problems: A taxonomy

    Pattern Recognition Letters, Vol. 69, pp. 49-55

2015

  1. A novel weakly supervised problem: Learning from positive-unlabeled proportions

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

  2. Contributions to learning Bayesian network models from weakly supervised data: Application to Assisted Reproductive Technologies and Software Defect Classification

    Contributions to learning Bayesian network models from weakly supervised data: Application to Assisted Reproductive Technologies and Software Defect Classification

  3. Multidimensional learning from crowds: Usefulness and application of expertise detection

    International Journal of Intelligent Systems, Vol. 30, Núm. 3, pp. 326-354

2013

  1. Learning Bayesian network classifiers from label proportions

    Pattern Recognition, Vol. 46, Núm. 12, pp. 3425-3440

  2. Learning from crowds in multi-dimensional classification domains

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

2011

  1. Learning naive Bayes models for multiple-instance learning with label proportions

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