ML Anthology

Target Curricula via Selection of Minimum Feature Sets: A Case Study in Boolean Networks

Shannon Fenn, Pablo Moscato
JMLR 2017 pp. 1-26
/jmlr/2017/fenn2017jmlr-target/

Abstract

We consider the effect of introducing a curriculum of targets when training Boolean models on supervised Multi Label Classification (MLC) problems. In particular, we consider how to order targets in the absence of prior knowledge, and how such a curriculum may be enforced when using meta-heuristics to train discrete non-linear models.

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Cite

Text

Fenn and Moscato. "Target Curricula via Selection of Minimum Feature Sets: A Case Study in Boolean Networks." Journal of Machine Learning Research, 2017.

Markdown

[Fenn and Moscato. "Target Curricula via Selection of Minimum Feature Sets: A Case Study in Boolean Networks." Journal of Machine Learning Research, 2017.](https://mlanthology.org/jmlr/2017/fenn2017jmlr-target/)

BibTeX

@article{fenn2017jmlr-target,
  title     = {{Target Curricula via Selection of Minimum Feature Sets: A Case Study in Boolean Networks}},
  author    = {Fenn, Shannon and Moscato, Pablo},
  journal   = {Journal of Machine Learning Research},
  year      = {2017},
  pages     = {1-26},
  volume    = {18},
  url       = {https://mlanthology.org/jmlr/2017/fenn2017jmlr-target/}
}