ML Anthology

Learning Sparse Perceptrons

Jeffrey C. Jackson, Mark Craven
NeurIPS 1995 pp. 654-660
/neurips/1995/jackson1995neurips-learning/

Abstract

We introduce a new algorithm designed to learn sparse percep(cid:173) trons over input representations which include high-order features. Our algorithm, which is based on a hypothesis-boosting method, is able to PAC-learn a relatively natural class of target concepts. Moreover, the algorithm appears to work well in practice: on a set of three problem domains, the algorithm produces classifiers that utilize small numbers of features yet exhibit good generalization performance. Perhaps most importantly, our algorithm generates concept descriptions that are easy for humans to understand.

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Cite

Text

Jackson and Craven. "Learning Sparse Perceptrons." Neural Information Processing Systems, 1995.

Markdown

[Jackson and Craven. "Learning Sparse Perceptrons." Neural Information Processing Systems, 1995.](https://mlanthology.org/neurips/1995/jackson1995neurips-learning/)

BibTeX

@inproceedings{jackson1995neurips-learning,
  title     = {{Learning Sparse Perceptrons}},
  author    = {Jackson, Jeffrey C. and Craven, Mark},
  booktitle = {Neural Information Processing Systems},
  year      = {1995},
  pages     = {654-660},
  url       = {https://mlanthology.org/neurips/1995/jackson1995neurips-learning/}
}