Optimizing the hyper-parameters for SVM by combining evolution strategies with a grid search

By Ruiming Liu, Erqi Liu, Jie Yang, Ming Li, Fanglin Wang. 2006

69 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Qiang Yang
2. Yuqiang Chen
3. Wei-Wei Tu
4. Wenyuan Dai
5. Yang Yu
6. Yi-Qi Hu
7. Mehdi Ghatee
8. Mohammad Mahdi Bejani

In Computer Vision and Pattern Recognition

1. Qiang Yang
2. Yang Yu
3. Wenyuan Dai
4. Yi-Qi Hu
5. Yuqiang Chen
6. Mehdi Ghatee
7. Wei-Wei Tu
8. Mohammad Mahdi Bejani

In Control and Systems Engineering

1. Yang Yu
2. Qiang Yang
3. Yi-Qi Hu
4. Mehdi Ghatee
5. Wei-Wei Tu
6. Mohammad Mahdi Bejani

Citations in the corpus

• Multi-Fidelity Automatic Hyper-Parameter Tuning via Transfer Series Expansion (2019, methodology): “Some derivative-free optimization methods (Liu et al. 2006; Bergstra and Bengio 2012; Shahriari et al. 2015; Hansen, Müller, and Koumoutsakos 2003; Hutter, Hoos, and Leyton-Brown 2011; Munos 2011; Bergstra et al. 2011; Yu, Qian, and Hu 2016) have been applied to solve this problem successfully.”
• A systematic review on overfitting control in shallow and deep neural networks (2021, methodology): “In a grid search proposed by Liu et al. (2006), an evolutionary algorithm is used to search, but it can be substituted by other meta-heuristic algorithms directly.”