Publicacions en què col·labora amb Francisco Charte Ojeda (13)

2022

  1. Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions

    IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, Núm. 12, pp. 9549-9560

2021

  1. Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study

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

  2. Slicer: feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study

    Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings

2020

  1. An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges

    Neurocomputing, Vol. 404, pp. 93-107

  2. Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

    Neurocomputing, Vol. 410, pp. 237-270

2019

  1. A Showcase of the Use of Autoencoders in Feature Learning Applications

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

  2. A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations

    Progress in Artificial Intelligence, Vol. 8, Núm. 1

  3. Ruta: Implementations of neural autoencoders in R

    Knowledge-Based Systems, Vol. 174, pp. 4-8

2018

  1. A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines

    Information Fusion, Vol. 44, pp. 78-96

  2. A practical tutorial on autoencoders for nonlinear feature fusion: taxonomy, models, software and guidelines

    XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018): avances en Inteligencia Artificial. 23-26 de octubre de 2018 Granada, España

  3. Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository

    Neurocomputing, Vol. 289, pp. 68-85

2016

  1. R ultimate multilabel dataset repository

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

2015

  1. Working with multilabel datasets in R: The mldr package

    R Journal, Vol. 7, Núm. 2, pp. 149-162