Learning / Recognizing a Distribution
It's pretty straight-forward to learn / categorize labels associated with a small collection of numerical or categorical attributes. However, is there a way to categorize distributions? I know attributes can be used to describe a distribution such as quantiles, median, mean, std. deviation, etc. But say I don't know which of these would be useful. Is there a learning algorithm that can be useful for classifying distribution? Maybe it's the case that a distribution that is skewed to the right always corresponds to some label. Is there an automated way to detect / learn this without having to have a human recognize that trait? I think optical character recognition would do something very similar...is there a way to classify distributions in RapidMiner? How would they be input?