当我尝试用 RBF 内核训练 SVM（trainsvm 函数）时，
libSVM 库在训练期间输出“在两类概率估计中行搜索失败” 。
训练后，模型的训练准确率仅为 20%。
我想我可能会错过一些东西，这与信息有关。
有关我的项目的更多信息，
我正在处理 PASCAL VOC 动作分类问题。
我正在尝试遵循这种方法。 http://www.ifp.illinois.edu/~jyang29/papers/CVPR09-ScSPM.pdf
有 1300 个训练图像和 11 个类。
制作码本和稀疏编码后，
特征向量的维数为 2688。
训练样例数为 1370。
You need to do a grid search, either using cross validation, or using a separate validation data set to get good values for C and gamma. Libsvm has a script called grid.py that is useful for this. I noticed you tagged this with matlab, using grid.py needs command line tools and a python installation (IMO this generally works out better than with matlab, especially if you have a some big machines to run many jobs in parallel).
I recommend that you read the libsvm guide if you haven't already done so: http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.
I also suggest you initially use the same dataset as used for the paper as occasionally published algorithms only work well on the dataset chosen for the paper.
Lastly, you could contact the authors of the paper.
我问了LIBSVM的作者这个警告，他回答说这个警告可以忽略。