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pro vyhledávání: '"Charte, David"'
Available data in machine learning applications is becoming increasingly complex, due to higher dimensionality and difficult classes. There exists a wide variety of approaches to measuring complexity of labeled data, according to class overlap, separ
Externí odkaz:
http://arxiv.org/abs/2111.06142
Autor:
Pascual-Triana, José Daniel, Charte, David, Arroyo, Marta Andrés, Fernández, Alberto, Herrera, Francisco
Publikováno v:
Knowledge and Information Systems (Knowl Inf Syst 63, 1961-1989 (2021))
Data Science and Machine Learning have become fundamental assets for companies and research institutions alike. As one of its fields, supervised classification allows for class prediction of new samples, learning from given training data. However, so
Externí odkaz:
http://arxiv.org/abs/2007.07935
Publikováno v:
Neurocomputing 404 (2020) 93-107
In many machine learning tasks, learning a good representation of the data can be the key to building a well-performant solution. This is because most learning algorithms operate with the features in order to find models for the data. For instance, c
Externí odkaz:
http://arxiv.org/abs/2005.10516
Publikováno v:
In: From Bioinspired Systems and Biomedical Applications to Machine Learning/IWINAC 2019. LNCS vol 11487. Springer (2019)
Autoencoders are techniques for data representation learning based on artificial neural networks. Differently to other feature learning methods which may be focused on finding specific transformations of the feature space, they can be adapted to fulf
Externí odkaz:
http://arxiv.org/abs/2005.04321
Publikováno v:
Charte, D., Charte, F., Garc\'ia, S. et al. Prog Artif Intell (2018)
Machine learning is a field which studies how machines can alter and adapt their behavior, improving their actions according to the information they are given. This field is subdivided into multiple areas, among which the best known are supervised le
Externí odkaz:
http://arxiv.org/abs/1811.12044
Autor:
Charte, Francisco, Rivera, Antonio J., Charte, David, del Jesus, María J., Herrera, Francisco
New proposals in the field of multi-label learning algorithms have been growing in number steadily over the last few years. The experimentation associated with each of them always goes through the same phases: selection of datasets, partitioning, tra
Externí odkaz:
http://arxiv.org/abs/1802.03568
Publikováno v:
Information Fusion 44 (2018) 78-96
Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model. The amount of these variables is also important, since performance tends to decline as th
Externí odkaz:
http://arxiv.org/abs/1801.01586
Autor:
Luengo, Julián, Moreno, Raúl, Sevillano, Iván, Charte, David, Peláez-Vegas, Adrián, Fernández-Moreno, Marta, Mesejo, Pablo, Herrera, Francisco
Publikováno v:
In Information Fusion February 2022 78:232-253
Autor:
Górriz, Juan M., Ramírez, Javier, Ortíz, Andrés, Martínez-Murcia, Francisco J., Segovia, Fermin, Suckling, John, Leming, Matthew, Zhang, Yu-Dong, Álvarez-Sánchez, Jose Ramón, Bologna, Guido, Bonomini, Paula, Casado, Fernando E., Charte, David, Charte, Francisco, Contreras, Ricardo, Cuesta-Infante, Alfredo, Duro, Richard J., Fernández-Caballero, Antonio, Fernández-Jover, Eduardo, Gómez-Vilda, Pedro, Graña, Manuel, Herrera, Francisco, Iglesias, Roberto, Lekova, Anna, de Lope, Javier, López-Rubio, Ezequiel, Martínez-Tomás, Rafael, Molina-Cabello, Miguel A., Montemayor, Antonio S., Novais, Paulo, Palacios-Alonso, Daniel, Pantrigo, Juan J., Payne, Bryson R., de la Paz López, Félix, Pinninghoff, María Angélica, Rincón, Mariano, Santos, José, Thurnhofer-Hemsi, Karl, Tsanas, Athanasios, Varela, Ramiro, Ferrández, Jose M.
Publikováno v:
In Neurocomputing 14 October 2020 410:237-270
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