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pro vyhledávání: '"Silvestrin, Luis P."'
Coping with distributional shifts is an important part of transfer learning methods in order to perform well in real-life tasks. However, most of the existing approaches in this area either focus on an ideal scenario in which the data does not contai
Externí odkaz:
http://arxiv.org/abs/2307.08572
With the development of new sensors and monitoring devices, more sources of data become available to be used as inputs for machine learning models. These can on the one hand help to improve the accuracy of a model. On the other hand, combining these
Externí odkaz:
http://arxiv.org/abs/2202.05069
Time-series forecasting plays an important role in many domains. Boosted by the advances in Deep Learning algorithms, it has for instance been used to predict wind power for eolic energy production, stock market fluctuations, or motor overheating. In
Externí odkaz:
http://arxiv.org/abs/2107.10709
Autor:
Silvestrin, Luis P., Pantiskas, Leonardos, Hoogendoorn, Mark, Nicosia, Giuseppe, Ojha, Varun, La Malfa, Emanuele, La Malfa, Gabriele, Jansen, Giorgio, Pardalos, Panos M., Giuffrida, Giovanni, Umeton, Renato
Publikováno v:
Machine Learning, Optimization, and Data Science ISBN: 9783030954666
Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part I, 1, 250-264
Silvestrin, L P, Pantiskas, L & Hoogendoorn, M 2022, A Framework for Imbalanced Time-Series Forecasting . in G Nicosia, V Ojha, E La Malfa, G La Malfa, G Jansen, P M Pardalos, G Giuffrida & R Umeton (eds), Machine Learning, Optimization, and Data Science : 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part I . vol. 1, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13163 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 250-264, 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, Virtual, Online, 4/10/21 . https://doi.org/10.1007/978-3-030-95467-3_19
Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part I, 1, 250-264
Silvestrin, L P, Pantiskas, L & Hoogendoorn, M 2022, A Framework for Imbalanced Time-Series Forecasting . in G Nicosia, V Ojha, E La Malfa, G La Malfa, G Jansen, P M Pardalos, G Giuffrida & R Umeton (eds), Machine Learning, Optimization, and Data Science : 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part I . vol. 1, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13163 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 250-264, 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, Virtual, Online, 4/10/21 . https://doi.org/10.1007/978-3-030-95467-3_19
Time-series forecasting plays an important role in many domains. Boosted by the advances in Deep Learning algorithms, it has for instance been used to predict wind power for eolic energy production, stock market fluctuations, or motor overheating. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39c9d177f627c3db3f120a6cb5c84fcb
https://research.vu.nl/en/publications/f697cf73-db33-45ee-a0e4-2c11dbefdccd
https://research.vu.nl/en/publications/f697cf73-db33-45ee-a0e4-2c11dbefdccd
Publikováno v:
Journal of Computational Mathematics and Data Science; December 2023, Vol. 9 Issue: 1
