Methods of construction of support vector machines do not require additional a priori information and can be used to process large scale data set. It is especially important for various problems in computational biology. The main set of algorithms of support vector machines for regression is presented. The comparative efficiency of a number of support-vector-algorithms for regression is investigated. A thorough analysis of the study results found the most efficient support vector algorithms for regression. The description of the presented algorithms, sufficient for their practical implementation is given.
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