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.

翻译：本文介绍了一种新颖的全套学习方法,称为“剩余相似森林 ” ( RLF ) 。 我们的弱小学习者产生有条件的可能性,这些可能性是按顺序优化的,在类似增殖的框架内(而不是根据观测到的数据衡量的概率分布)利用以往学习者的全球损失进行顺序优化,并且是多倍(而不是累加性)的组合。这提高了我们强大的分类器的效率,允许设计在模型能力方面更为紧凑的分类器。我们将我们的方法应用于若干机器学习分类任务,显示业绩有显著改善。与包括随机森林和渐进型植树在内的几种混合方法相比,RLF在同时缩小所需模型规模的同时,也提供了显著的绩效改进。