David Charte

Orcid: 0000-0002-4830-9512

According to our database1, David Charte authored at least 15 papers between 2015 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2022
Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges.
Inf. Fusion, 2022

2021
Revisiting data complexity metrics based on morphology for overlap and imbalance: snapshot, new overlap number of balls metrics and singular problems prospect.
Knowl. Inf. Syst., 2021

Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study.
Proceedings of the Hybrid Artificial Intelligent Systems - 16th International Conference, 2021

2020
COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images.
IEEE J. Biomed. Health Informatics, 2020

Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications.
Neurocomputing, 2020

An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges.
Neurocomputing, 2020

2019
A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations.
Prog. Artif. Intell., 2019

Ruta: Implementations of neural autoencoders in R.
Knowl. Based Syst., 2019

A Showcase of the Use of Autoencoders in Feature Learning Applications.
Proceedings of the From Bioinspired Systems and Biomedical Applications to Machine Learning, 2019

2018
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines.
Inf. Fusion, 2018

Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository.
Neurocomputing, 2018

A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods.
CoRR, 2018

2016
R Ultimate Multilabel Dataset Repository.
Proceedings of the Hybrid Artificial Intelligent Systems - 11th International Conference, 2016

2015
Working with Multilabel Datasets in R: The mldr Package.
R J., 2015


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