20. The method of claim 19 , further comprising providing the pruned set of training candidate/object feature value pairs to the plurality of models and wherein training each logistic regression model to predict a likelihood that a candidate matches the test object comprises training each model only on the pruned set of training candidate/object feature value pairs.
19. The method of claim 15 , further comprising pruning the set of training candidate/object feature value pairs to form a pruned set of training candidate/object feature value pairs by decreasing the training candidate/object feature value pairs included in the set, wherein the pruning includes: determining by the data processing apparatus that an occurrence of a test candidate attribute value in the set of training candidate/object feature value pairs satisfies a first threshold; determining by the data processing apparatus that a relative frequency of the test candidate attribute value in a determined set of positive and negative cases of the test candidate/object feature value pairs satisfies a second threshold; and keeping the candidate/object feature value pair corresponding to the test candidate attribute value in the set of training candidate/object feature value pairs in response to determining that the occurrence satisfies the first threshold and the relative frequency satisfies the second threshold.