【作者】 付阳；

【导师】 廖频；

【作者基本信息】 南昌大学 ， 计算机系统结构， 2010， 硕士

【摘要】 随着支持向量机的广泛应用,其在大数据集上训练效率问题以及通过参数优化提高其性能的问题受到人们的广泛关注,本文就这两个问题做了以下研究：首先提出了一种基于多核并行的支持向量机并行训练算法用来解决其在大数据集上的训练效率问题。该并行训练算法以LIBSVM为基础,对其中的核矩阵计算、更新梯度、工作集选择模块均进行了并行化处理,并使用OpenMP、Intel线程构建模块、Intel并行函数库等多核并行工具和技术对其进行实现。其次提出了一种基于嵌套式遗传算法的SVM参数优化方法。该方法首先针对核函数参数优化构建遗传算法,在其适用度函数中针对惩罚因子优化问题构建遗传算法,用惩罚因子遗传算法搜索到的解的训练结果作为其适应度值。实验表明,该方法比普通的基于遗传算法的参数优化方法有着更好优化性能。最后通过以上两种方法有效的提高了支持向量机的效率和性能,并将其应用在人脸性别识别系统中,获得了较好的效果。更多还原

【Abstract】 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.更多还原