1. A method for use with a first classification model that classifies an input into one of a plurality of classes, wherein the first classification model was built using labeled training data, wherein the labeled training data comprises a plurality of items of labeled training data, wherein each of the plurality of items of labeled training data is labeled with one of the plurality of classes, the method comprising acts of: obtaining unlabeled input for the first classification model; building a similarity model that represents similarities between the unlabeled input and the labeled training data; and using a programmed processor and the similarity model to evaluate the labeled training data to identify a subset of the plurality of items of labeled training data that is more similar to the unlabeled input than a remainder of the labeled training data.