Just wondering the effects of additional features. Following are several thoughts:
If the additional features are noisy (can not distinguish the two classes), then additional features won't hurt SVM. Because SVM's final hyperplane will parallel to the additional dimension.
If the additional features happen to provide some distinguish power on the training data, SVM will use these features to grasp some unreal trend, thus these additional features will increase SVM's generalization error.
If there are a lot of training data, then the probability that a feature happen to distinguish the two classes is low.
If you add too many features, then the probability that these additional features together happen to distinguish the two classes is high.
Am I correct?