Diversity and degrees of freedom in regression ensembles

@article{Reeve2018DiversityAD,
  title={Diversity and degrees of freedom in regression ensembles},
  author={Henry W. J. Reeve and Gavin Brown},
  journal={Neurocomputing},
  year={2018},
  volume={298},
  pages={55-68}
}
  • Henry W. J. Reeve, Gavin Brown
  • Published 2018
  • Computer Science, Mathematics
  • Neurocomputing
  • Ensemble methods are a cornerstone of modern machine learning. The performance of an ensemble depends crucially upon the level of diversity between its constituent learners. This paper establishes a connection between diversity and degrees of freedom (i.e. the capacity of the model), showing that diversity may be viewed as a form of inverse regularisation. This is achieved by focusing on a previously published algorithm Negative Correlation Learning (NCL), in which model diversity is explicitly… CONTINUE READING

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