An Experimental Study of the Combination of Meta-Learning with Particle Swarm Algorithms for SVM Parameter Selection

By Péricles Barbosa C. de Miranda, Ricardo Bastos Cavalcante Prudêncio, André Carlos Ponce de Leon Ferreira de Carvalho, Carlos Soares. 2012

14 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural 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. Marco F. Huber
2. Marc-André Zöller
3. Wei-Wei Tu
4. Quanming Yao
5. Yang Yu
6. Qiang Yang
7. Yi-Qi Hu
8. Isabelle M Guyon

In Civil and Structural Engineering

1. Marc-André Zöller
2. Qiang Yang
3. Marco F. Huber
4. Yu-Feng Li

Citations in the corpus

• Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “Warm-starting can also be used for many aspects of AutoML, yet most research focuses on model selection and tuning (Gomes et al., 2012; De Miranda et al., 2012; Reif et al., 2012; Feurer et al., 2015a, 2015b; Wistuba et al., 2015b; Lindauer & Hutter, 2018).”
• Survey on Automated Machine Learning (2019, methodology): “Warm-starting can be used for many aspects of AutoML, yet most research focuses on model selection and tuning (De Miranda et al., 2012; Reif et al., 2012; Feurer et al., 2015a,b; Wistuba et al., 2015b; Lindauer and Hutter, 2018).”
• Taking Human out of Learning Applications: A Survey on Automated Machine Learning (2018, background): “Most work of this kind focus on hyperparameter tuning, with particle swarm optimization [115], [116], evolutionary algorithm [117], and sequential model-based optimization [5], [118], [119].”