JOURNAL OF INEQUALITIES AND APPLICATIONS, 2013
Background: Support vector machines, one of the non-parametric controlled classifiers, is a two-class classification method introduced in the context of statistical learning theory and structural risk minimization. Support vector machines are basically divided into two groups as linear support vector machines and nonlinear support vector machines. Nonlinear support vector machines are designed to make classifications by creating a plane in a space by mapping data to that higher dimensional input space. This method basically involves solving a quadratic programming problem. In this study, the support vector machines, which have an increasing rate of use in pattern recognition area, are used in the quality control of DNA sequencing data. Consequently, the classification of quality of all the DNA sequencing data will automatically be made as 'high quality/low quality'.