ML Anthology

Model Selection via the VC Dimension

Merlin Mpoudeu, Bertrand Clarke
JMLR 2019 pp. 1-26
/jmlr/2019/mpoudeu2019jmlr-model/

Abstract

We derive an objective function that can be optimized to give an estimator for the Vapnik-Chervonenkis dimension for use in model selection in regression problems. We verify our estimator is consistent. Then, we verify it performs well compared to seven other model selection techniques. We do this for a variety of types of data sets.

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Cite

Text

Mpoudeu and Clarke. "Model Selection via the VC Dimension." Journal of Machine Learning Research, 2019.

Markdown

[Mpoudeu and Clarke. "Model Selection via the VC Dimension." Journal of Machine Learning Research, 2019.](https://mlanthology.org/jmlr/2019/mpoudeu2019jmlr-model/)

BibTeX

@article{mpoudeu2019jmlr-model,
  title     = {{Model Selection via the VC Dimension}},
  author    = {Mpoudeu, Merlin and Clarke, Bertrand},
  journal   = {Journal of Machine Learning Research},
  year      = {2019},
  pages     = {1-26},
  volume    = {20},
  url       = {https://mlanthology.org/jmlr/2019/mpoudeu2019jmlr-model/}
}