搜索资源列表
svmfenlei
- 利用支持向量机实现非线性分类，通过调节参数改变分类个数-Nonlinear SVM classification, change classification number by adjusting the parameters
SVM
- 基于SVM的回归预测——上证指数开盘指数预测,包括数据的提取和预处理，选择回归预测分析最佳的SVM参数c&g，利用回归预测分析最佳的参数进行SVM网络训练-SVM-Based Forecasting
chapter15_0
- 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
chapter15_PSO
- 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
chapter15_GA
- 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
gaSVMcgForClass
- 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