Abstract
Methods of construction of support vector machines (SVMs) require no additional a priori information and allow large volumes of multidimensional data to be processed, which is especially important for solving various problems in computational biology. The main algorithms of SVM construction for binary classification are reviewed. The issue of the quality of the SVM learning algorithms is considered, and a description of proposed algorithms is given that is sufficient for their practical implementation. Comparative analysis of the efficiency of support vector classifiers is presented.
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Original Russian Text © N.O. Kadyrova, L.V. Pavlova, 2015, published in Biofizika, 2015, Vol. 60, No. 1, pp. 18–31.
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Kadyrova, N.O., Pavlova, L.V. Comparative efficiency of algorithms based on support vector machines for binary classification. BIOPHYSICS 60, 13–24 (2015). https://doi.org/10.1134/S0006350915010145
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DOI: https://doi.org/10.1134/S0006350915010145