Universidad Carlos III de Madrid-ko ikertzaileekin lankidetzan egindako argitalpenak (5)

2020

  1. Ensembles of cost-diverse Bayesian neural learners for imbalanced binary classification

    Information Sciences, Vol. 520, pp. 31-45

  2. MNIST-NET10: A heterogeneous deep networks fusion based on the degree of certainty to reach 0.1% error rate. ensembles overview and proposal

    Information Fusion, Vol. 62, pp. 73-80

2017

  1. Class Switching according to Nearest Enemy Distance for learning from highly imbalanced data-sets

    Pattern Recognition, Vol. 70, pp. 12-24

2015

  1. Classification of binary imbalanced data using a bayesian ensemble of bayesian neural networks

    Communications in Computer and Information Science

2014

  1. Fast fingerprint identification for large databases

    Pattern Recognition, Vol. 47, Núm. 2, pp. 588-602