Model selection of SVMs using GA approach

By Peng-Wei Chen, Jung-Ying Wang, Hahn-Ming Lee. 2004

60 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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. Kwok-Wing Chau
2. Jianyi Lin
3. Chun-tian Cheng
4. Shenglian Guo

In Computer Vision and Pattern Recognition

1. Kwok-Wing Chau
2. Jianyi Lin
3. Chun-tian Cheng

Citations in the corpus

• Benchmark and Survey of Automated Machine Learning Frameworks (2019, neutral): “, tuning C and γ of a support-vector machine (SVM) (Chen et al., 2004), it was proven that guided search strategies yield better results than grid search in less time.”
• Survey on Automated Machine Learning (2019, neutral): “However, these concepts cannot be easily applied to algorithm selection: a crossover between a SVM and a decision tree cannot be modeled reasonably.”
• Using support vector machines for long-term discharge prediction (2006, methodology): “, 2005), genetic algorithms (Chen et al., 2004), the shuffled complex evolution algorithm (Yu et al.”
• Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir (2014, methodology): “There are many methods to select these parameters, such as genetic algorithms (Chen et al. 2004), the stepwise search (Dong et al. 2005), SCE-UA algorithm (Lin et al. 2006).”