Hothorn T
Torsten Hothorn, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, 8001 Zürich, Switzerland, E-Mail:
Methods Inf Med. 2014;53(6):417-8. doi: 10.3414/ME13-10-0123.
This editorial is part of a For-Discussion-Section of Methods of Information in Medicine about the papers "The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling" and "Extending Statistical Boosting - An Overview of Recent Methodological Developments", written by Andreas Mayr and co-authors. It preludes two discussed reviews on developments and applications of boosting in biomedical research. The two review papers, written by Andreas Mayr, Harald Binder, Olaf Gefeller, and Matthias Schmid, give an overview on recently published methods that utilise gradient or likelihood-based boosting for fitting models in the life sciences. The reviews are followed by invited comments by experts in both boosting theory and applications.
本社论是《医学信息方法》关于“提升算法的演变——从机器学习到统计建模”和“扩展统计提升——近期方法发展概述”两篇论文的讨论板块的一部分,作者是安德烈亚斯·迈尔及其他合著者。它为关于提升算法在生物医学研究中的发展与应用的两篇讨论性综述做了铺垫。这两篇综述论文由安德烈亚斯·迈尔、哈拉尔德·宾德、奥拉夫·格费勒和马蒂亚斯·施密德撰写,概述了近期发表的利用梯度提升或基于似然的提升来拟合生命科学模型的方法。综述之后是提升理论和应用方面专家的特邀评论。