Support vector machines (SVM) in near infrared (NIR) spectroscopy: Focus on parameters optimization and model interpretation

By O. Devos, Cyril Ruckebusch, A. Durand, Ludovic Duponchel, Jean-Pierre Huvenne. 2009

261 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Biophysics

View Paper: https://www.semanticscholar.org/paper/e5c0546d98c5ef8d8aed2d5da0d2cdfc3160d302

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1. A. Casson
2. Ning-ning Wang
3. Feiren Wang
4. Emma Gowen
5. Ellen Poliakoff
6. Guangjun Qiu
7. Enli Lü
8. Andrius Vabalas

Citations in the corpus

• Machine learning algorithm validation with a limited sample size (2019, methodology): “[19] To control for overfitting logistic regression was regularised using L1 (Lasso) or L2 (Ridge) penalty terms and the magnitude of penalty was controlled by regularization parameter C.”
• Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis (2019, methodology): “As a supervised machine learning algorithm, the SVM function can efficiently handle linear and nonlinear data for regression or classification purposes [40].”