  This work proposes a new way of combining independently trained classifiers
over space and time. Combination over space means that the outputs of spatially
distributed classifiers are aggregated. Combination over time means that the
classifiers respond to streaming data during testing and continue to improve
their performance even during this phase. By doing so, the proposed
architecture is able to improve prediction performance over time with unlabeled
data. Inspired by social learning algorithms, which require prior knowledge of
the observations distribution, we propose a Social Machine Learning (SML)
paradigm that is able to exploit the imperfect models generated during the
learning phase. We show that this strategy results in consistent learning with
high probability, and it yields a robust structure against poorly trained
classifiers. Simulations with an ensemble of feedforward neural networks are
provided to illustrate the theoretical results.
