Classifiers follow two main approaches. Using the first approach, a classifier trains on all available data, and attempts to create a model of the entire space. The second approach is to find patterns in the data, and create a model of a subset of the space that matches the pattern\cite{Mannila02}. Global classifiers have the benefit of increased training data and faster runtimes, while local classifiers have the benefit of decreased noise and training data that is more similar to the testing data.

In the following sections, I will explore the locality and non-locality based classifiers in detail.
