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pro vyhledávání: '"Bonetti, Paolo"'
Multi-task learning (MTL) is a powerful machine learning paradigm designed to leverage shared knowledge across tasks to improve generalization and performance. Previous works have proposed approaches to MTL that can be divided into feature learning,
Externí odkaz:
http://arxiv.org/abs/2406.07991
Machine learning algorithms are designed to capture complex relationships between features. In this context, the high dimensionality of data often results in poor model performance, with the risk of overfitting. Feature selection, the process of sele
Externí odkaz:
http://arxiv.org/abs/2310.11059
Many real-world machine learning applications are characterized by a huge number of features, leading to computational and memory issues, as well as the risk of overfitting. Ideally, only relevant and non-redundant features should be considered to pr
Externí odkaz:
http://arxiv.org/abs/2306.11143
One of the central issues of several machine learning applications on real data is the choice of the input features. Ideally, the designer should select only the relevant, non-redundant features to preserve the complete information contained in the o
Externí odkaz:
http://arxiv.org/abs/2303.14734
Autor:
Bonetti, Paolo, author
Publikováno v:
Terrorism and the Foreigner: A Decade of Tension around the Rule of Law in Europe. 11:295-324
Publikováno v:
Data Mining & Knowledge Discovery; Jul2024, Vol. 38 Issue 4, p1713-1781, 69p
Autor:
Acquati, Francesco, Bertilaccio, Sabrina, Grimaldi, Annalisa, Monti, Laura, Cinquetti, Raffaella, Bonetti, Paolo, Lualdi, Marta, Vidalino, Laura, Fabbri, Marco, Sacco, Maria Grazia, van Rooijen, Nico, Campomenosi, Paola, Vigetti, Davide, Passi, Alberto, Riva, Cristina, Capella, Carlo, Sanvito, Francesca, Doglioni, Claudio, Gribaldo, Laura, Macchi, Paolo, Sica, Antonio, Noonan, Douglas M., Ghia, Paolo, Taramelli, Roberto, Klein, George
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2011 Jan . 108(3), 1104-1109.
Externí odkaz:
https://www.jstor.org/stable/25770916
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