The DOI matches its Crossref record; 140 of the 140 references it cites were found in a registry or an index.
A Survey of Optimization Methods From a Machine Learning Perspective
Open accessAbstract
Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, has attracted much attention of researchers. With the exponential growth of data amount and the increase of model complexity, optimization methods in machine learning face more and more challenges. A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively. The systematic retrospect and summary of the optimization methods from the perspective of machine learning are of great significance, which can offer guidance for both developments of optimization and machine learning research. In this article, we first describe the optimization problems in machine learning. Then, we introduce the principles and progresses of commonly used optimization methods. Finally, we explore and give some challenges and open problems for the optimization in machine learning.
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Cites
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Cited by
Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022) · Sensors · doi:10.3390/s22062181 · Cited by 93
“This procedure is computed by gradient descent, for which various optimisation methods exist (such as Stochastic Gradient Descent (SGD), minibatch gradient descent method (MSGD), and Adaptive moment estimation (ADAM), amongst others [55]).”
MethodA comprehensive survey and taxonomy on privacy-preserving deep learning
Tran, A.-T., Luong, T.-D., & Huynh, V.-N. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127345 · Cited by 21
Metadata
- Authors
- Sun, S., Cao, Z., Zhu, H., & Zhao, J.
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2020-08
- Volume
- 50
- Issue
- 8
- Pages
- 3668-3681
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W2990346675
- Cited by
- 789
- References
- 140
- FWCI
- 29.70
- Type
- journal article
- Language
- en
- Open access
- green
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