How to classify parametric curves?
Whenever you have skewed dataset, it means that you know some classes better than some others. In such cases it means that the data is your knowledge and there are learning algorithms for such occasions.

Consider an important fact here. Suppose that you have feature vectors of conditions of a nuclear company and they describe whether the company is in danger of nuclear radiation or not. In such case it is clear that it does not happen a lot that you have infected companies so most of your data have label of healthy condition. You have so much knowledge about the healthy class but you don't know much about the infected class because you don't have much data consequently you don't know its distribution and you can not estimate it well. Whenever your data is skewed, it means that e.g. you have 1 million feature vectors of negative class and 5 feature vectors of positive class. Now suppose that you change the feature vectors. In such cases you have imbalanced data-set or you just have the data samples of some classes without some other, you can use anomaly detection.


  In data mining, anomaly detection (also outlier detection) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.