文件名称:out-matlab
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介绍说明－－下载内容均来自于网络，请自行研究使用
SVM回归，用于实现支持向量机（SVM
）回归拟合的问题。可以用来做一些短期的预测，如短期负荷预测。-svm regression, used to implement support vector machine (SVM
) Regression fitting problems. Can be used to do some short-term forecasts, such as short-term load forecasting.
）回归拟合的问题。可以用来做一些短期的预测，如短期负荷预测。-svm regression, used to implement support vector machine (SVM
) Regression fitting problems. Can be used to do some short-term forecasts, such as short-term load forecasting.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
| 文件名 | 大小 | 更新时间 |
|---|---|---|
| 43_1PointShift | ||
| ..............\linear | ||
| ..............\......\1.fig | ||
| ..............\......\2.fig | ||
| ..............\......\4.fig | ||
| ..............\......\data_ns.mat | ||
| ..............\......\data_s.mat | ||
| ..............\RBF | ||
| ..............\...\1.fig | ||
| ..............\...\2.fig | ||
| ..............\...\3.fig | ||
| ..............\...\4.fig | ||
| ..............\...\data_s.mat | ||
| 43_1PointShift.mat | ||
| 43_20PointShift.mat | ||
| 98.mat | ||
| 98.xls | ||
| 98_1PointShift.mat | ||
| 98_20PointShift.mat | ||
| b15.mat | ||
| b15.xls | ||
| b15_1PointShift.mat | ||
| b15_20PointShift.mat | ||
| b15_30PointShift.mat | ||
| bat.asv | ||
| bat.m | ||
| ca.mat | ||
| ca.xls | ||
| ca_1PointShift.mat | ||
| ca_20PointShift.mat | ||
| CorssV.asv | ||
| CrossValidation.asv | ||
| CrossValidation.m | ||
| data43.mat | ||
| Easy.asv | ||
| libsvmread.c | ||
| libsvmread.mexw32 | ||
| libsvmwrite.c | ||
| libsvmwrite.mexw32 | ||
| make.m | ||
| Makefile | ||
| README | ||
| scale.asv | ||
| ScaleStart.asv | ||
| ScaleStart.m | ||
| SplitAndScale.asv | ||
| svm.obj | ||
| svm_model_matlab.c | ||
| svm_model_matlab.h | ||
| svm_model_matlab.obj | ||
| SVMcg.asv | ||
| SVMcg.m | ||
| svmpredict.c | ||
| svmpredict.mexw32 | ||
| svmtrain.asv | ||
| svmtrain.c | ||
| svmtrain.mexw32 | ||
| TrainAndTest.asv | ||
| UnscaleStart.asv | ||
| UnscaleStart.m | ||
| 不预测纯拟合，训练集43f(1-5000) | 测试集43f(5001-end).fig | |
| 不预测纯拟合，训练集43f(1-5000) | 测试集43f(5001-end).jpg | |
| '第一组数据20点偏移，无归一化，线性核函数 | -c 4 -g 32.fig | |
| 43_20PointShift | ||
| ...............\linear | ||
| ...............\......\1.fig | ||
| ...............\......\2.fig | ||
| ...............\......\4.fig | ||
| ...............\......\data_ns.mat | ||
| ...............\......\data_s.mat | ||
| ...............\RBF | ||
| ...............\...\1.fig | ||
| ...............\...\2.fig | ||
| ...............\...\3.fig | ||
| ...............\...\4.fig | ||
| ...............\...\data_ns.mat | ||
| ...............\...\data_s.mat | ||
| 98_1PointShift | ||
| ..............\RBF | ||
| ..............\...\1.fig | ||
| ..............\...\2.fig | ||
| ..............\...\data_s.mat | ||
| ..............\...\CrossValidation | ||
| ..............\...\...............\1.fig | ||
| ..............\...\...............\2.fig | ||
| ..............\...\...............\3.fig | ||
| ..............\...\...............\4.fig | ||
| ..............\...\...............\最优参数下结果（没什么大区别）.fig | ||
| ..............\...\...............\源参数结果.fig | ||
| 98_20PointShift | ||
| ...............\RBF | ||
| ...............\...\1.fig | ||
| ...............\...\2.fig | ||
| ...............\...\data_s.mat | ||
| ...............\...\CrossValidation | ||
| ...............\...\...............\1.fig | ||
| ...............\...\...............\2.fig | ||
| ...............\...\...............\3.fig | ||
| ...............\...\...............\4.fig | ||
| ...............\...\...............\最优参数下结果（没什么大区别）.fig |