ML Anthology

Sample Complexity for Learning Recurrent Perceptron Mappings

Bhaskar DasGupta, Eduardo D. Sontag
NeurIPS 1995 pp. 204-210
/neurips/1995/dasgupta1995neurips-sample/

Abstract

Recurrent perceptron classifiers generalize the classical perceptron model. They take into account those correlations and dependences among input coordinates which arise from linear digital filtering. This paper provides tight bounds on sample complexity associated to the fitting of such models to experimental data.

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Cite

Text

DasGupta and Sontag. "Sample Complexity for Learning Recurrent Perceptron Mappings." Neural Information Processing Systems, 1995.

Markdown

[DasGupta and Sontag. "Sample Complexity for Learning Recurrent Perceptron Mappings." Neural Information Processing Systems, 1995.](https://mlanthology.org/neurips/1995/dasgupta1995neurips-sample/)

BibTeX

@inproceedings{dasgupta1995neurips-sample,
  title     = {{Sample Complexity for Learning Recurrent Perceptron Mappings}},
  author    = {DasGupta, Bhaskar and Sontag, Eduardo D.},
  booktitle = {Neural Information Processing Systems},
  year      = {1995},
  pages     = {204-210},
  url       = {https://mlanthology.org/neurips/1995/dasgupta1995neurips-sample/}
}