Abstract
In this chapter, we discuss the machine learning paradigm of Support Vector Machines which incorporates the principles of Empirical Risk Minimization and Structural Risk Minimization. Support Vector Machines constitute a state-of-the-art classifier which is used as a benchmark algorithm to evaluate the classification accuracy of Artificial Immune System-based machine learning algorithms. In this chapter, we also present a special class of Support Vector Machines that are especially designed for the problem of One-Class Classification, namely the One-Class Support Vector Machines.
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Sotiropoulos, D.N., Tsihrintzis, G.A. (2017). Machine Learning Paradigms. In: Machine Learning Paradigms. Intelligent Systems Reference Library, vol 118. Springer, Cham. https://doi.org/10.1007/978-3-319-47194-5_5
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DOI: https://doi.org/10.1007/978-3-319-47194-5_5
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