@phdthesis{180,
  author = {V. Gueorguiev and Deshen Moodley},
  title = {Hyperparameter Optimization for Astronomy},
  abstract = {The task of phenomenon classification in astronomy provides a novel and challenging setting for the application of state-of-the-art techniques addressing the problem of combined
algorithm selection and hyperparameter optimization (CASH) of machine learning algorithms, which find local applications such as at the data-intensive Square Kilometre Array
(SKA). This work will use various algorithms for CASH to explore the possibility and efficacy of hyperparameter optimization on improving performance of machine learning
techniques for astronomy. Then, with focus on the Galaxy Zoo project, these algorithms will be used to conduct an indepth comparison of state-of-the-art in hyperparameter optimization
(HPO) along with techniques that aim to improve performance on large datasets and expensive function evaluations. Finally, the likelihood for an integration with a cognitive
vision system for astronomy will be examined by conducting a brief exploration into different feature extraction and selection methods.},
  year = {2017},
  volume = {Honours},
  publisher = {University of Cape Town},
  url = {http://projects.cs.uct.ac.za/honsproj/cgi-bin/view/2017/gueorguiev_henhaeyono_stopforth.zip/#downloads},
}