论文标题
残留的可能性森林
Residual Likelihood Forests
论文作者
论文摘要
本文提出了一种新颖的合奏学习方法，称为残留可能性森林（RLF）。我们的弱学习者会产生有条件的可能性，这些可能性在以前的学习者的背景下在类似于增强的框架（而不是从观察到的数据中测量的概率分布）中依次优化，并且是乘法性的（而不是加性）。这提高了我们强大的分类器的效率，从而使分类器的设计在模型容量方面更紧凑。我们将方法应用于几个机器学习分类任务，显示出绩效的显着改善。与几种合奏方法进行比较，包括随机森林和梯度增强树，RLF可以显着改善性能，同时减少所需的模型大小。
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.
