论文标题
基于树的方法及其组合的研究
A study of tree-based methods and their combination
论文作者
论文摘要
基于树的方法是在各个领域使用的流行机器学习技术。在这项工作中，我们回顾了他们的基础和一般框架，重要性采样了学习集合（ISLE），以加速其合适过程。此外，我们通过混合（ARM）来描述一种称为自适应回归的模型组合策略，该策略对于通过Isle的基于树的方法是可行的。此外，提出了三个修改的小岛，并在真实数据集上评估其性能。
Tree-based methods are popular machine learning techniques used in various fields. In this work, we review their foundations and a general framework the importance sampled learning ensemble (ISLE) that accelerates their fitting process. Furthermore, we describe a model combination strategy called the adaptive regression by mixing (ARM), which is feasible for tree-based methods via ISLE. Moreover, three modified ISLEs are proposed, and their performance are evaluated on the real data sets.
