Definition
Support vector machines (SVMs) are particular linear classifiers which are based on the margin maximization principle. They perform structural risk minimization, which improves the complexity of the classifier with the aim of achieving excellent generalization performance. The SVM accomplishes the classification task by constructing, in a higher dimensional space, the hyperplane that optimally separates the data into two categories.
Introduction
Considering a two-category classification problem, a linear classifier separates the space, with a hyperplane, into two regions, each of which is also called a class. Before the creation of SVMs, the popular algorithm for determining the parameters of a linear classifier was a single-neuron perceptron. The perceptron algorithm uses an updating rule to generate a separating surface for a two-class problem. The procedure is guaranteed to converge when the training...
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Adankon, M., Cheriet, M. (2009). Support Vector Machine. In: Li, S.Z., Jain, A. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73003-5_299
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DOI: https://doi.org/10.1007/978-0-387-73003-5_299
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