ML Anthology

Boosting First-Order Learning

J. Ross Quinlan
ALT 1996 pp. 143-155
doi:10.1007/3-540-61863-5_42 /alt/1996/quinlan1996alt-boosting/

Abstract

Several empirical studies have confirmed that boosting classifier-learning systems can lead to substantial improvements in predictive accuracy. This paper reports early experimental results from applying boosting to ffoil , a first-order system that constructs definitions of functional relations. Although the evidence is less convincing than that for propositional-level learning systems, it suggests that boosting will also prove beneficial for first-order induction.

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Text

Quinlan. "Boosting First-Order Learning." International Conference on Algorithmic Learning Theory, 1996. doi:10.1007/3-540-61863-5_42

Markdown

[Quinlan. "Boosting First-Order Learning." International Conference on Algorithmic Learning Theory, 1996.](https://mlanthology.org/alt/1996/quinlan1996alt-boosting/) doi:10.1007/3-540-61863-5_42

BibTeX

@inproceedings{quinlan1996alt-boosting,
  title     = {{Boosting First-Order Learning}},
  author    = {Quinlan, J. Ross},
  booktitle = {International Conference on Algorithmic Learning Theory},
  year      = {1996},
  pages     = {143-155},
  doi       = {10.1007/3-540-61863-5_42},
  url       = {https://mlanthology.org/alt/1996/quinlan1996alt-boosting/}
}