Web: http://arxiv.org/abs/2206.11263

June 24, 2022, 1:10 a.m. | Patrick Echtenbruck, Martina Echtenbruck, Joost Batenburg, Thomas Bäck, Boris Naujoks, Michael Emmerich

cs.LG updates on arXiv.org arxiv.org

Automated model selection is often proposed to users to choose which machine
learning model (or method) to apply to a given regression task. In this paper,
we show that combining different regression models can yield better results
than selecting a single ('best') regression model, and outline an efficient
method that obtains optimally weighted convex linear combination from a
heterogeneous set of regression models. More specifically, in this paper, a
heuristic weight optimization, used in a preceding conference paper, is
replaced …

applications arxiv lg models optimization regression

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY