It partially depends on whether the data / distributions are separable or overlapping.
Assuming the data is separable (it probably isn't), the numbers don't matter too much as long as you have exemplars (support vectors) which lie close to the margin boundary and hence determine the decision boundary. Generally the more data you train with the better as you'll have a richer pot of potential support vectors and the relative numbers don't matter.
If the data is not separable, the numbers should typically reflect the prior class probabilities, i.e. how the examples are drawn from the real world. You give an example where about 6% are class 0 and 94% are class 1. If this reflects the fact that class 0 examples are much rarer in real life than class 1, then this is appropriate. However, if the classes are very overlapping (based on your choice of features), it may be that the classifier would just learn to say class 1 all the time as that would be right 94% of the time, but it would not be predictive in any sense. So if you have imbalanced class distributions as you seem to suggest, make sure that the features have enough discriminatory power to predict the rare class in some cases.