1. A computer-implemented pattern recognition method comprising: obtaining a trainable recognition system using one or more computers; obtaining a first item to be recognized comprising one or more units to be recognized; obtaining, using the one or more computers, a plurality of hypotheses for class labels of said one or more units; creating, using the one or more computers, a plurality of trained model variants for said trainable recognition system by training said recognition system based at least in part on said first item to be recognized, and labeling each of the trained model variants respectively with the class labels based at least in part on a different one of the hypotheses in said plurality of hypotheses for the class of labels of said one or more units; obtaining a set of practice data with labels; performing recognition, using the one or more computers, of said practice data respectively using each of said plurality of trained model variants to obtain recognition results; measuring performance, using the one or more computers, of each of said trained model variants based at least in part on the recognition results obtained for the set of practice data and the labels for the practice data; determining, using the one or more computers, one of said trained model variants with a best measured performance; and selecting, using the one or more computers, as the class labels for said first item to be recognized the class labels of said one or more units associated with the one trained model variant determined to have the best measured performance, thereby using the hypothesis that among the plurality of hypotheses has a best measured performance.