Massively-parallel best subset selection for ordinary least-squares regression

Abstract:
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SEEK ID: https://publications.h-its.org/publications/207

DOI: 10.1109/SSCI.2017.8285225

Research Groups: Astroinformatics

Publication type: InProceedings

Journal: 2017 IEEE Symposium Series on Computational Intelligence (SSCI)

Book Title: 2017 IEEE Symposium Series on Computational Intelligence (SSCI)

Citation: 2017 IEEE Symposium Series on Computational Intelligence (SSCI),pp.1-8,IEEE

Date Published: 1st Nov 2017

Registered Mode: imported from a bibtex file

Authors: Fabian Gieseke, Kai Lars Polsterer, Ashish Mahabal, Christian Igel, Tom Heskes

Citation
Gieseke, F., Polsterer, K. L., Mahabal, A., Igel, C., & Heskes, T. (2017). Massively-parallel best subset selection for ordinary least-squares regression. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1–8). IEEE. https://doi.org/10.1109/ssci.2017.8285225
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Created: 7th Sep 2019 at 10:22

Last updated: 5th Mar 2024 at 21:23

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