ML Anthology

Monotonic Networks

Joseph Sill
NeurIPS 1997 pp. 661-667
/neurips/1997/sill1997neurips-monotonic/

Abstract

Monotonicity is a constraint which arises in many application do(cid:173) mains. We present a machine learning model, the monotonic net(cid:173) work, for which monotonicity can be enforced exactly, i.e., by virtue offunctional form . A straightforward method for implementing and training a monotonic network is described. Monotonic networks are proven to be universal approximators of continuous, differen(cid:173) tiable monotonic functions. We apply monotonic networks to a real-world task in corporate bond rating prediction and compare them to other approaches.

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Cite

Text

Sill. "Monotonic Networks." Neural Information Processing Systems, 1997.

Markdown

[Sill. "Monotonic Networks." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/sill1997neurips-monotonic/)

BibTeX

@inproceedings{sill1997neurips-monotonic,
  title     = {{Monotonic Networks}},
  author    = {Sill, Joseph},
  booktitle = {Neural Information Processing Systems},
  year      = {1997},
  pages     = {661-667},
  url       = {https://mlanthology.org/neurips/1997/sill1997neurips-monotonic/}
}