Mostrando ítems 11-15 de 27

    • Improving Medical Data Annotation Including Humans in the Machine Learning Loop 

      Bobes-Bascarán, José; Mosqueira-Rey, E.; Alonso Ríos, David (MDPI, 2021)
      [Abstract] At present, the great majority of Artificial Intelligence (AI) systems require the participation of humans in their development, tuning, and maintenance. Particularly, Machine Learning (ML) systems could greatly ...
    • Case Study of Anomaly Detection and Quality Control of Energy Efficiency and Hygrothermal Comfort in Buildings 

      Eiras-Franco, Carlos; Flores, Miguel; Bolón-Canedo, Verónica; Zaragoza, Sonia; Fernández-Casal, Rubén; Naya, Salvador; Tarrío-Saavedra, Javier (2019)
      [Abstract] The aim of this work is to propose different statistical and machine learning methodologies for identifying anomalies and control the quality of energy efficiency and hygrothermal comfort in buildings. ...
    • On the Effectiveness of Convolutional Autoencoders on Image-Based Personalized Recommender Systems 

      Blanco Mallo, Eva; Remeseiro, Beatriz; Bolón-Canedo, Verónica; Alonso-Betanzos, Amparo (MDPI AG, 2020-08-19)
      [Abstract] Over the years, the success of recommender systems has become remarkable. Due to the massive arrival of options that a consumer can have at his/her reach, a collaborative environment was generated, where users ...
    • An Intelligent and Collaborative Multiagent System in a 3D Environment 

      Rodríguez Arias, Alejandro; Guijarro-Berdiñas, Bertha; Sánchez-Maroño, Noelia (MDPI AG, 2020-08-21)
      [Abstract] Multiagent systems (MASs) allow facing complex, heterogeneous, distributed problems difficult to solve by only one software agent. The world of video games provides problems and suitable environments for the use ...
    • Regression Tree Based Explanation for Anomaly Detection Algorithm 

      López-Riobóo Botana, Iñigo Luis; Eiras-Franco, Carlos; Alonso-Betanzos, Amparo (MDPI AG, 2020-08-18)
      [Abstract] This work presents EADMNC (Explainable Anomaly Detection on Mixed Numerical and Categorical spaces), a novel approach to address explanation using an anomaly detection algorithm, ADMNC, which provides accurate ...