Do you know why the SVM performs poorly on test data that has a different class distribution than the training data? The training data has around 15 classes, and the additional testing data has around 6 classes (a subset of 15 classes). I found that the accuracy of new testing data is around 3% (all predicted labels belong to the same class).
How can I deal with this problem?
