ML Anthology

Learning from Infinite Data in Finite Time

Pedro Domingos, Geoff Hulten
NeurIPS 2001 pp. 673-680
/neurips/2001/domingos2001neurips-learning/

Abstract

We propose the following general method for scaling learning algorithms to arbitrarily large data sets. Consider the model Mii learned by the algorithm using ni examples in step i (ii = (nl , ... ,nm)) , and the model Moo that would be learned using in(cid:173) finite examples. Upper-bound the loss L(Mii' M oo ) between them as a function of ii, and then minimize the algorithm's time com(cid:173) plexity f(ii) subject to the constraint that L(Moo , Mii ) be at most f with probability at most 8. We apply this method to the EM algorithm for mixtures of Gaussians. Preliminary experiments on a series of large data sets provide evidence of the potential of this approach.

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Cite

Text

Domingos and Hulten. "Learning from Infinite Data in Finite Time." Neural Information Processing Systems, 2001.

Markdown

[Domingos and Hulten. "Learning from Infinite Data in Finite Time." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/domingos2001neurips-learning/)

BibTeX

@inproceedings{domingos2001neurips-learning,
  title     = {{Learning from Infinite Data in Finite Time}},
  author    = {Domingos, Pedro and Hulten, Geoff},
  booktitle = {Neural Information Processing Systems},
  year      = {2001},
  pages     = {673-680},
  url       = {https://mlanthology.org/neurips/2001/domingos2001neurips-learning/}
}