Abstract
The learning process of support vector machine algorithms leads to solving a convex quadratic programming problem. Since this optimization problem has a unique solution and also satisfies the Karush–Kuhn–Tucker conditions, it can be solved very efficiently. In this chapter, the formulation of optimization problems which have arisen in the various forms of support vector machine algorithms is discussed.
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Parand, K., Baharifard, F., Aghaei, A.A., Jani, M. (2023). Basics of SVM Method and Least Squares SVM. In: Rad, J.A., Parand, K., Chakraverty, S. (eds) Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines. Industrial and Applied Mathematics. Springer, Singapore. https://doi.org/10.1007/978-981-19-6553-1_2
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DOI: https://doi.org/10.1007/978-981-19-6553-1_2
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