A Parallel Training Algorithm Of Support Vector Machines And Parameter Optimization Based On Genetic Algorithm

With the extensive application of support vector machines, the efficiency of its training with large-data and improve its performance through the optimization problems are brought to wide attention.In this paper, two problems do the following research:Frist of all, this paper present a multi-core parallel based support vector machine parallel training algorithm used to improve the efficiency of training with large-data. The parallel training algorithm based on LIBSVM, on which the nuclear matrix calculation, update gradient, worked-sample selection modules for parallel processing. Using OpenMP,Intel Threading Building Blocks,Intel multi-core parallel libraries and other tools and techniques to achieve them.Secondly, this paper was proposed based on genetic algorithm nested parameters optimization method of SVM. In this method, Kernel Parameter Optimization for the construction of genetic algorithm, in its application to function in the penalty factor for the construction of genetic algorithm optimization, genetic algorithms using penalty factor the optimal solution to the training result as the fitness value. Experiments show that this method is better than ordinary based on the genetic algorithm is optimized parameters optimization method has better performance.Finally, through the above two methods improves the support vector machine efficiency and performance, and its application in the face of gender recognition system, good results were obtained.