Abstract
A support vector machine (SVM) is a binary classifier. It abstracts a decision boundary in multi-dimensional space using an appropriate sub-set of the training set of vectors; the elements of this sub-set are the support vectors. Geometrically, support vectors are those training patterns that are closest to the decision boundary. In order to appreciate the behaviour of SVMs, it is useful to understand several associated concepts including linear discriminant functions and neural networks. So, we introduce these concepts before describing SVMs.
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© 2011 Universities Press (India) Pvt. Ltd.
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Murty, M.N., Devi, V.S. (2011). Support Vector Machines. In: Pattern Recognition. Undergraduate Topics in Computer Science, vol 0. Springer, London. https://doi.org/10.1007/978-0-85729-495-1_7
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DOI: https://doi.org/10.1007/978-0-85729-495-1_7
Publisher Name: Springer, London
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