Publicacións nas que colabora con FRANCISCO DAVID CHARTE LUQUE (20)

2022

  1. A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges

    Information Fusion

  2. A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges

    Information Fusion, Vol. 78, pp. 232-253

  3. Mejoras en tratamiento de problemas de clasificación con modelos basados en autoencoders

    Mejoras en tratamiento de problemas de clasificación con modelos basados en autoencoders

  4. 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. Revisiting data complexity metrics based on morphology for overlap and imbalance: snapshot, new overlap number of balls metrics and singular problems prospect

    Knowledge and Information Systems, Vol. 63, Núm. 7, pp. 1961-1989

  2. 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)

  3. 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

  3. COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images

    IEEE Journal of Biomedical and Health Informatics, Vol. 24, Núm. 12, pp. 3595-3605

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 showcase of the use of autoencoders in feature learning applications

    From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II (Springer Suiza), pp. 412-421

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

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

  4. 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. Nuevas arquitecturas hardware de procesamiento de alto rendimiento para aprendizaje profundo

    Enseñanza y aprendizaje de ingeniería de computadores: Revista de Experiencias Docentes en Ingeniería de Computadores, Núm. 8, pp. 67-84

  4. 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