Beside SVM, what are the classification models that can be trained by a dataset of only positive training examples? and which of these models are generally known to perform better in such cases?
UPDATE: I mean problems that are described by the following quoted sentences:
- "One-class SVM is an unsupervised algorithm that learns a decision function for novelty detection: classifying new data as similar or different to the training set".
- "But what if you only have data of one class and the goal is to test new data and found out whether it is alike or not like the training data?".