1. A computer-implemented method of optimizing a classification algorithm, comprising: receiving, at a processor, a set of trained weighted feature functions for the classification algorithm, and a plurality of training instances; calculating an evaluation sequence for applying the trained weighted feature functions to the plurality of training instances that orders the feature functions in accordance with a measure of influence on the classification algorithm; determining at least one classification threshold for each step of the evaluation sequence, wherein each classification threshold indicates whether a classification decision can be made at an associated step of the evaluation sequence and the classification algorithm terminated without evaluating further feature functions; and storing the evaluation sequence and classification thresholds at a storage device.