It is clear from such a definition that unusual event de- tection entails a number of challenges. The rarity of an un- usual event means that collecting sufficient training data for supervised learning will often be infeasible, necessitating methods for learning from small numbers of examples. In addition, more than one type of unusual event may occur in a given data sequence, where the event types can be ex- pected to differ markedly from one another. This implies that training a single model to capture all unusual events will generally be infeasible, further exacerbating the prob- lem of learning from limited data. As well as such mod- eling problems due to.
