svm 的参数优化，利用交叉验证法选择最优参数c g，最终提高训练集的分类准确率,更好的提高分类器性能-Svm parameter optimization, the use of cross-validation method to the optimal parameter c g, and ultimately improve the training set classification accuracy，better improve the classifier performan

svm 的参数优化，利用pso(粒子群优化算法)选择最优参数c g，最终提高训练集的分类准确率，更好的提高分类器性能-Svm parameter optimization, the use of pso (particle swarm optimization algorithm) to the optimal parameter c g, and ultimately improve the training set classification accuracy, better impr

svm 的参数优化，利用ga(遗传优化算法)选择最优参数c g，最终提高训练集的分类准确率，更好的提高分类器性能-Svm parameter optimization, the use of ga (genetic optimization algorithm) to the optimal parameter c g, and ultimately improve the accuracy of the training set classification, better improve

svm 的参数优化，利用ga(遗传优化算法)选择最优参数c g，最终提高训练集的分类准确率，更好的提高分类器性能,这是ga的功能函数源码-Svm parameter optimization, the use of ga (genetic optimization algorithm) to the optimal parameter c g, and ultimately improve the training set classification accuracy, better imp