Global optimization issues in deep network regression: an overview
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DOI: 10.1007/s10898-018-0701-7
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Cited by:
- Carrizosa, Emilio & Ramírez-Ayerbe, Jasone & Romero Morales, Dolores, 2024. "Mathematical optimization modelling for group counterfactual explanations," European Journal of Operational Research, Elsevier, vol. 319(2), pages 399-412.
- Liu, Longlong & Zhou, Suyu & Jie, Qian & Du, Pei & Xu, Yan & Wang, Jianzhou, 2024. "A robust time-varying weight combined model for crude oil price forecasting," Energy, Elsevier, vol. 299(C).
- Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.
- Tommaso Colombo & Massimiliano Mangone & Andrea Bernetti & Marco Paoloni & Valter Santilli & Laura Palagi, 2019. "Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis," DIAG Technical Reports 2019-08, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
- Corrado Coppola & Lorenzo Papa & Marco Boresta & Irene Amerini & Laura Palagi, 2024. "Tuning parameters of deep neural network training algorithms pays off: a computational study," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(3), pages 579-620, October.
- Laura Palagi & Ruggiero Seccia, 2020. "Block layer decomposition schemes for training deep neural networks," Journal of Global Optimization, Springer, vol. 77(1), pages 97-124, May.
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Keywords
Supervised learning; Deep networks; Feedforward neural networks; Global optimization; Weights optimization; Hybrid algorithms;All these keywords.
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