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A Comprehensive Survey of Loss Functions in Machine Learning

Author

Listed:
  • Qi Wang

    (University of Chinese Academy of Sciences
    Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Yue Ma

    (University of Chinese Academy of Sciences
    Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Kun Zhao

    (Beijing Wuzi University)

  • Yingjie Tian

    (Chinese Academy of Sciences
    Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

Abstract

As one of the important research topics in machine learning, loss function plays an important role in the construction of machine learning algorithms and the improvement of their performance, which has been concerned and explored by many researchers. But it still has a big gap to summarize, analyze and compare the classical loss functions. Therefore, this paper summarizes and analyzes 31 classical loss functions in machine learning. Specifically, we describe the loss functions from the aspects of traditional machine learning and deep learning respectively. The former is divided into classification problem, regression problem and unsupervised learning according to the task type. The latter is subdivided according to the application scenario, and here we mainly select object detection and face recognition to introduces their loss functions. In each task or application, in addition to analyzing each loss function from formula, meaning, image and algorithm, the loss functions under the same task or application are also summarized and compared to deepen the understanding and provide help for the selection and improvement of loss function.

Suggested Citation

  • Qi Wang & Yue Ma & Kun Zhao & Yingjie Tian, 2022. "A Comprehensive Survey of Loss Functions in Machine Learning," Annals of Data Science, Springer, vol. 9(2), pages 187-212, April.
    • Handle: RePEc:spr:aodasc:v:9:y:2022:i:2:d:10.1007_s40745-020-00253-5
    DOI: 10.1007/s40745-020-00253-5
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    References listed on IDEAS

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    1. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    2. Lee, Yoonkyung & Lin, Yi & Wahba, Grace, 2004. "Multicategory Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 67-81, January.
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    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. Shuangyue Wang & Ziyan Luo, 2023. "Sparse Support Tensor Machine with Scaled Kernel Functions," Mathematics, MDPI, vol. 11(13), pages 1-20, June.
    5. 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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