libsvm cmd line，weka和matlab之间的精度差异
[英]libsvm differences in accuracy between cmd line, weka and matlab
问题描述
I'm doing some preliminary testing with 2 classes of vectors, trying to separate them with libsvm. 
我正在使用2类向量进行一些初步测试，尝试使用libsvm将它们分开。 I get a 78.2% correct ID rate in Matlab and at the cmd line (using libsvm), but in Weka I get around 95%. 我在Matlab和cmd行（使用libsvm）获得了78.2％的正确ID率，但是在Weka中，我得到了95％的正确ID率。
No cross-validation was done in Weka; 在Weka中没有交叉验证； just trained model and then read in test dataset and classified it. 只是训练过的模型，然后读取测试数据集并将其分类。
Can anyone offer an explanation? 谁能提供解释？ Thanks in advance. 提前致谢。
1 个解决方案
解决方案1
1 2012-08-11 11:31:22
If you didn't provide a separate Test Data , the validation Folds should be set, 10 or desired value. 如果未提供单独的测试数据，则应将验证折数设置为10或所需的值。 however, be sure that the same SVMType and kerneltype are being used in both program. 但是，请确保两个程序都使用相同的SVMType和kerneltype 。 by default Weka uses C-SVC with radial basis function. 默认情况下，Weka使用具有径向基函数的C-SVC 。
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