Journal
ANALYTICA CHIMICA ACTA
Volume 665, Issue 2, Pages 129-145Publisher
ELSEVIER
DOI: 10.1016/j.aca.2010.03.030
Keywords
Support vector machine; Least squares support vector machine; Kernel logistic regression; Kernel-based learning; Feature selection; Multi-class probabilities
Categories
Funding
- Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen)
- Research Council KUL: GOA-AMBioRICS [IDO 05/010 EEG-fMRI, GOA/2004105]
- Flemish Government: FWO [G.0360.05, G.0519.06, G.0341.07, G.0321. 06, G.0302.07, G.0566.06]
- IWT
- Belgian Federal Government: DWTC [IUAP IV-02, IUAP V-22]
- Dynamical Systems and Control: Computation, Identification Modelling
- Belgian Federal Science Policy Office [IUAP P6/04]
- EU: BIOPATTERN [FP6-2002-IST 508803]
- eTUMOUR [FP6-2002-LIFESCIHEALTH 503094]
- HealthAgents [FP6-2005-IST 027213]
- FAST [FP6-019279-2]
- ESA: Cardiovascular Control [Prodex-8 C90242]
Ask authors/readers for more resources
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. (C) 2010 Elsevier B.V. All rights reserved.
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