Applicational aspects of support vector machines

By A. Belousov, S. Verzakov, J. von Frese. 2002

178 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Biophysics

View Paper: https://www.semanticscholar.org/paper/4334d85e8b18ac69dc72bb010df2149d8de3160b

How researchers see this paper

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

1. Jean-Pierre Huvenne
2. Cyril Ruckebusch
3. Ludovic Duponchel
4. A. Durand
5. O. Devos
6. Andrius Vabalas

Citations in the corpus

• Support vector machines (SVM) in near infrared (NIR) spectroscopy: Focus on parameters optimization and model interpretation (2009, methodology): “The SVM have been introduced in chemometrics only recently [5] and have been successfully applied for mid and near infrared classification tasks, such as material identification [6,7] and food discrimination [8–11].”
• Landslide susceptibility assessment using SVM machine learning algorithm (2011, unknown): “Originally, SVM is a binary classifier (instances could be classified to only one of the two classes), but one can easily transform n-class problems into the sequence of n (one-versus-all) or n(n−1)/2 (one-versus-one) binary classification tasks (Belousov et al., 2002).”