Support vector machines and its applications in chemistry

By Hong-Dong Li, Yizeng Liang, Qingsong Xu. 2009

334 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/21229ae9a15a5b43e971a3d8e6a7a73fee59e49b

How researchers see this paper

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

In Analytical Chemistry

1. Robert Powers
2. Bradley Worley

In Computational Theory and Mathematics

1. Robert Powers
2. Bradley Worley

In Control and Systems Engineering

1. Klaus-Dieter Thoben
2. Robert Powers
3. Christopher Irgens
4. Daniel Weimer
5. Bradley Worley
6. Thorsten Wuest

Citations in the corpus

• Multivariate Analysis in Metabolomics. (2012, methodology): “Support-vector machine (SVM) methods [118] have been applied to human urine metabolomics NMR [119], NIR and UV datasets [120] with enhanced predictive power over PLS-DA.”
• Machine learning in manufacturing: advantages, challenges, and applications (2016, background): “Further application areas include but are not limited to credit rating (Huang, Chen, Hsu, Chen, & Wu, 2004), food quality control (Borin, Ferrão, Mello, Maretto, & Poppi, 2006), classification of polymers (Li et al., 2009), and rule extraction (Martens, Baesens, Van Gestel, & Vanthienen, 2007).”