A tutorial on support vector machine-based methods for classification problems in chemometrics.
This tutorial provides a concise overview of support vector machines and different closely related techniques for pattern classification. The tutorial starts with the formulation of support vector machines for classification. The method of least squares support vector machines is explained. Approaches to retrieve a probabilistic interpretation are covered and it is explained how the binary classification techniques can be extended to multi-class methods. Kernel logistic regression, which is closely related to iteratively weighted least squares support vector machines, is discussed. Different practical aspects of these methods are addressed: the issue of feature selection, parameter tuning, unbalanced data sets, model evaluation and statistical comparison. The different concepts are illustrated on three real-life applications in the field of metabolomics, genetics and proteomics.
Luts
Jan
J
Department of Electrical Engineering (ESAT), Research Division SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium. jan.luts@esat.kuleuven.be
Ojeda
Fabian
F
Van de Plas
Raf
R
De Moor
Bart
B
Van Huffel
Sabine
S
Suykens
Johan A K
JA
D000465:Algorithms; D001185:Artificial Intelligence; D001932:Brain Neoplasms / Q000145:classification; D007090:Image Interpretation, Computer-Assisted; D016018:Least-Squares Analysis; D016015:Logistic Models; D010363:Pattern Recognition, Automated / Q000379:methods*