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Continuous exploitative measurement trajectories using Bayesian optimisation

Rémi Delanghe (UGent) , Tom Van Steenkiste (UGent) , Dirk Deschrijver (UGent) and Tom Dhaene (UGent)
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Please use this url to cite or link to this publication:

MLA
Delanghe, Rémi, et al. “Continuous Exploitative Measurement Trajectories Using Bayesian Optimisation.” 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Abstracts, 2019.
APA
Delanghe, R., Van Steenkiste, T., Deschrijver, D., & Dhaene, T. (2019). Continuous exploitative measurement trajectories using Bayesian optimisation. In 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Abstracts. Brussels, Belgium.
Chicago author-date
Delanghe, Rémi, Tom Van Steenkiste, Dirk Deschrijver, and Tom Dhaene. 2019. “Continuous Exploitative Measurement Trajectories Using Bayesian Optimisation.” In 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Abstracts.
Chicago author-date (all authors)
Delanghe, Rémi, Tom Van Steenkiste, Dirk Deschrijver, and Tom Dhaene. 2019. “Continuous Exploitative Measurement Trajectories Using Bayesian Optimisation.” In 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Abstracts.
Vancouver
1.
Delanghe R, Van Steenkiste T, Deschrijver D, Dhaene T. Continuous exploitative measurement trajectories using Bayesian optimisation. In: 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Abstracts. 2019.
IEEE
[1]
R. Delanghe, T. Van Steenkiste, D. Deschrijver, and T. Dhaene, “Continuous exploitative measurement trajectories using Bayesian optimisation,” in 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Abstracts, Brussels, Belgium, 2019.
@inproceedings{8632313,
  author       = {Delanghe, Rémi and Van Steenkiste, Tom and Deschrijver, Dirk and Dhaene, Tom},
  booktitle    = {28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Abstracts},
  language     = {eng},
  location     = {Brussels, Belgium},
  pages        = {2},
  title        = {Continuous exploitative measurement trajectories using Bayesian optimisation},
  year         = {2019},
}