A Comprehensive Survey of Loss Functions in Machine Learning
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DOI: 10.1007/s40745-020-00253-5
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- Loutfi, Ahmad Amine & Sun, Mengtao & Loutfi, Ijlal & Solibakke, Per Bjarte, 2022. "Empirical study of day-ahead electricity spot-price forecasting: Insights into a novel loss function for training neural networks," Applied Energy, Elsevier, vol. 319(C).
- Emma King-Smith & Felix A. Faber & Usa Reilly & Anton V. Sinitskiy & Qingyi Yang & Bo Liu & Dennis Hyek & Alpha A. Lee, 2024. "Predictive Minisci late stage functionalization with transfer learning," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
- Shuangyue Wang & Ziyan Luo, 2023. "Sparse Support Tensor Machine with Scaled Kernel Functions," Mathematics, MDPI, vol. 11(13), pages 1-20, June.
- Henrik Seckler & Ralf Metzler, 2022. "Bayesian deep learning for error estimation in the analysis of anomalous diffusion," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
- Iqbal H. Sarker, 2023. "Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects," Annals of Data Science, Springer, vol. 10(6), pages 1473-1498, December.
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Keywords
Loss function; Machine learning; Deep learning; Survey;All these keywords.
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