1. A system, comprising: a processor circuit, including: a first data input configured to receive probability estimates from two or more separate feature classifiers over a collection of training items, those items having associated ground truth category information; and a processor-readable medium, including instructions that, when performed by the processor, configure the system to: select a fusion model to adapt local statistics of the two or more separate feature classifiers over the collection of training items; generate K partitions on each separate feature classifier to form K 2 pairs of associations; determine a maximum likelihood estimate of a pair of the K 2 pairs being the correct classifier including modelling the likelihood using a localized expert forest and using a linear model for the localized expert forest; and fuse the maximum likelihood estimates from the separate feature classifiers according to the selected fusion model to generate an output probability estimate for new items that do not have associated ground truth information.