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Issue 2, 2010
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Support Vector Machines for classification and regression

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The increasing interest in Support Vector Machines (SVMs) over the past 15 years is described. Methods are illustrated using simulated case studies, and 4 experimental case studies, namely mass spectrometry for studying pollution, near infrared analysis of food, thermal analysis of polymers and UV/visible spectroscopy of polyaromatic hydrocarbons. The basis of SVMs as two-class classifiers is shown with extensive visualisation, including learning machines, kernels and penalty functions. The influence of the penalty error and radial basis function radius on the model is illustrated. Multiclass implementations including one vs. all, one vs. one, fuzzy rules and Directed Acyclic Graph (DAG) trees are described. One-class Support Vector Domain Description (SVDD) is described and contrasted to conventional two- or multi-class classifiers. The use of Support Vector Regression (SVR) is illustrated including its application to multivariate calibration, and why it is useful when there are outliers and non-linearities.

Graphical abstract: Support Vector Machines for classification and regression

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Publication details

The article was received on 11 Sep 2009, accepted on 30 Nov 2009 and first published on 23 Dec 2009

Article type: Tutorial Review
DOI: 10.1039/B918972F
Citation: Analyst, 2010,135, 230-267

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    Support Vector Machines for classification and regression

    R. G. Brereton and G. R. Lloyd, Analyst, 2010, 135, 230
    DOI: 10.1039/B918972F

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