Imagine I am using an SVM to train a classifier for a given dataset, in one-vs-all configuration. For each class, I am performing cross validation for parameter selection (grid search to choose parameters for both SVM and kernel). I am also using the RBF kernel.
In many cases, what I am observing is that the number of support vectors for the positive class exactly matches (or is very close to) the total number of positive examples. The support vectors for the negative class vary, but is still quite high.
Thus, my questions are:
1) For a linear case, is it reasonable to expect that the number of support vectors from the positive set would be significantly less (given that the data is apparently not linearly seperable).
2) Does the number of support vectors selected reflect classification "capacity" in the sense that a high capacity classifier can separate highly overlapping classes with a complex decision boundary? Is it reasonable to assume that a higher number of support vectors is indicative of high capacity?