An empirical study of Monte Carlo-based methods in machine learning

Fei Lei; Yan Wang; Lulu Wang; Qiuping Wu

doi:10.1117/12.3012141

21 December 2023 An empirical study of Monte Carlo-based methods in machine learning

Fei Lei, Yan Wang, Lulu Wang, Qiuping Wu

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129702X (2023) https://doi.org/10.1117/12.3012141
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Empirical studies in the field of machine learning based on Monte Carlo methods explore a powerful statistical technique aimed at solving the core problems of uncertainty modeling, model evaluation, and decision making; Monte Carlo methods provide new perspectives and tools for the application of machine learning models through stochastic sampling and simulation; however, Monte Carlo methods are also facing a number of challenges, such as computational efficiency, high-dimensional space problems, and sampling bias, etc.; in order to overcome these problems, this paper explores future research directions that may involve improved sampling methods, innovative applications that incorporate deep learning, etc. Therefore, the continuous innovation in this field will provide new opportunities and challenges for the development and application of machine learning models.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fei Lei, Yan Wang, Lulu Wang, and Qiuping Wu "An empirical study of Monte Carlo-based methods in machine learning", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129702X (21 December 2023); https://doi.org/10.1117/12.3012141

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