  This paper presents a novel ensemble learning approach called Residual
Likelihood Forests (RLF). Our weak learners produce conditional likelihoods
that are sequentially optimized using global loss in the context of previous
learners within a boosting-like framework (rather than probability
distributions that are measured from observed data) and are combined
multiplicatively (rather than additively). This increases the efficiency of our
strong classifier, allowing for the design of classifiers which are more
compact in terms of model capacity. We apply our method to several machine
learning classification tasks, showing significant improvements in performance.
When compared against several ensemble approaches including Random Forests and
Gradient Boosted Trees, RLFs offer a significant improvement in performance
whilst concurrently reducing the required model size.
