A biologist’s introduction to support vector machines
نویسنده
چکیده
The support vector machine (SVM) is a pattern recognition algorithm that has been used to analyze an increasing variety of complex biological data sets, including microarray expression profiles, DNA and protein sequences, protein-protein interaction networks, tandem mass spectra, etc. This tutorial describes the algorithm in a nontechnical fashion, using as an example a leukemia microarray expression data set. Four components of the SVM are described in turn: the separating hyperplane, the maximum margin hyperplane, the soft margin and the kernel function. The aim of the tutorial is to allow the non-specialist to determine whether an SVM would be appropriate for a given analysis task and to provide them with sufficient intuitions to apply existing SVM software to the task.
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