Now showing items 1-3 of 3

    • Fitting the data from embryo implantation prediction: Learning from label proportions 

      Hernández-González J.; Inza I.; Crisol-Ortíz L.; Guembe MA.; Iñarra MJ.; Lozano J.A. (Statistical Methods in Medical Research, 2016-01-01)
      Machine learning techniques have been previously used to assist clinicians to select embryos for human-assisted reproduction. This work aims to show how an appropriate modeling of the problem can contribute to improve ...
    • Optimization of deep learning precipitation models using categorical binary metrics 

      Larraondo P.R.; Renzullo L.J.; Van Dijk A.I.J.M.; Inza I.; Lozano J.A. (Journal of Advances in Modeling Earth Systems, 2020)
      This work introduces a methodology for optimizing neural network models using a combination of continuous and categorical binary indices in the context of precipitation forecasting. Probability of detection or false alarm ...
    • Soft range information for network localization 

      Mazuelas S.; Conti A.; Allen J.C.; Win M.Z. (IEEE Transactions on Signal Processing, 2018-06-15)
      The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements. Conventional range-based localization approaches rely on ...