Application of gradient boosted trees to gender prediction based on motivations of masters athletes-Reference-Cited by-同舟云学术

Application of gradient boosted trees to gender prediction based on motivations of masters athletes

Published:2018-08-01 Issue:3 Volume:13 Page:235-252
ISSN:1574-1699
Container-title:Model Assisted Statistics and Applications
language:
Short-container-title:MAS

Author:

Walsh Joe¹,Heazlewood Ian¹,Climstein Mike²³⁴

Affiliation:

1. School of Environmental and Life Sciences, Charles Darwin University, Darwin, Northern Territory, Australia

2. Exercise, Health and Performance Faculty Research Group, Faculty of Health Sciences The University of Sydney, Sydney, New South Wales, Australia

3. Institute of Health and Sport, Bond University, Robina, Queensland, Australia

4. School of Health and Human Sciences, Southern Cross University, Lismore, Australia

Publisher

IOS Press

Subject

Applied Mathematics,Modeling and Simulation,Statistics and Probability

Reference56 articles.

1. Benner, A. (2002). Application of “aggregated classifiers” in survival time studies. In Compstat (pp. 171-176). Physica, Heidelberg.

2. Bergstra, J.S., Bardenet, R., Bengio, Y., & Kégl, B. (2011). Algorithms for hyper-parameter optimization. In Advances in Neural Information Processing Systems (pp. 2546-2554).

3. Random search for hyper-parameter optimization;Bergstra,;Journal of Machine Learning Research,2012

4. Does depression cause obesity? A meta-analysis of longitudinal studies of depression and weight control;Blaine,;Journal of Health Psychology,2008

5. Differentiating mass participant sport event consumers: traditional versus non-traditional events;Buning,;Sport Marketing Quarterly,2016

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