A lightweight knowledge-based PSO for SVM hyper-parameters tuning in a dynamic environment-Reference-Cited by-同舟云学术

A lightweight knowledge-based PSO for SVM hyper-parameters tuning in a dynamic environment

Published:2023-05-19 Issue:16 Volume:79 Page:18777-18799
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Kalita Dhruba Jyoti,Singh Vibhav Prakash,Kumar Vinay

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-023-05385-y.pdf

Reference36 articles.

1. Yang L, Shami A (2020) On hyperparameter optimization of machine learning algorithms: theory and practice. Neurocomputing 415:295–316

2. Kalita DJ, Singh VP, Kumar V (2020) A survey on SVM hyper-parameters optimization techniques. In: Social networking and computational intelligence: proceedings of SCI-2018. Springer Singapore. (pp. 243-256)

3. Eberhart R, Kennedy J (1995) Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, (Vol. 4, pp. 1942–1948)

4. Vapnik V, Vapnik V (1998) Statistical learning theory (pp. 156–160)

5. Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L, Lopez A (2020) A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing 408:189–215

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