Comparative Analysis of Krylov Iterative Methods in Support Vector Machines
نویسندگان
چکیده
Data mining and classification is a growing and important field in bioinformatics. Machine learning algorithms such as support vector machines can be used with genetic information to predict disease susceptibility. In particular, single nucleotide polymorphisms have been analyzed to classify an individual into "sick" or "healthy" categories for a specific genetic disorder. The most computationally intensive part of the support vector machine algorithm involves solving a quadratic programming problem through the use of an iterative solver. This research examines various iterative solving methods that are utilized within support vector machines. In such a solver, the solution of the problem is obtained through successively converging on an optimal result. These solvers are analyzed based on efficiency and the accuracy of the classification.
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