Search

Your search keyword '"Tobias Hepp"' showing total 2 results
Start Over Author "Tobias Hepp" Journal computational and mathematical methods in medicine
2 results on '"Tobias Hepp"'

1. Corrigendum to 'Probing for Sparse and Fast Variable Selection with Model-Based Boosting'

Author

Tobias Hepp, Andreas Mayr, Janek Thomas, and Bernd Bischl

Subjects
0301 basic medicine, Boosting (machine learning), General Immunology and Microbiology, business.industry, Computer science, Applied Mathematics, Feature selection, 02 engineering and technology, General Medicine, Machine learning, computer.software_genre, lcsh:Computer applications to medicine. Medical informatics, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 030104 developmental biology, Modeling and Simulation, 0202 electrical engineering, electronic engineering, information engineering, lcsh:R858-859.7, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer
Published
2018

2. An Update on Statistical Boosting in Biomedicine

Author

Elisabeth Waldmann, Olaf Gefeller, Benjamin Hofner, Tobias Hepp, Sebastian Meyer, and Andreas Mayr

Subjects
0301 basic medicine, FOS: Computer and information sciences, Boosting (machine learning), Biomedical Research, Computer science, Feature selection, Machine Learning (stat.ML), Review Article, Machine learning, computer.software_genre, lcsh:Computer applications to medicine. Medical informatics, 01 natural sciences, Statistics - Applications, Statistics - Computation, General Biochemistry, Genetics and Molecular Biology, 010104 statistics & probability, 03 medical and health sciences, Statistics - Machine Learning, Humans, Applications (stat.AP), 0101 mathematics, Biomedicine, Computation (stat.CO), Models, Statistical, General Immunology and Microbiology, business.industry, Applied Mathematics, Statistical model, General Medicine, Regression, 030104 developmental biology, Modeling and Simulation, lcsh:R858-859.7, Functional regression, Artificial intelligence, business, computer, Algorithms
Abstract

Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely flexible, as the underlying base-learners (regression functions defining the type of effect for the explanatory variables) can be combined with any kind of loss function (target function to be optimized, defining the type of regression setting). In this review article, we highlight the most recent methodological developments on statistical boosting regarding variable selection, functional regression, and advanced time-to-event modelling. Additionally, we provide a short overview on relevant applications of statistical boosting in biomedicine.

Published
2017

Catalog

Books, media, physical & digital resources
See catalog results