ML Anthology

A Framework for Average Case Analysis of Conjunctive Learning Algorithms

Michael J. Pazzani, Wendy Sarrett
MLJ 1992 pp. 349-372
doi:10.1007/BF00994111 /mlj/1992/pazzani1992mlj-framework/

Abstract

We present an approach to modeling the average case behavior of learning algorithms. Our motivation is to predict the expected accuracy of learning algorithms as a function of the number of training examples. We apply this framework to a purely empirical learning algorithm, (the one-sided algorithm for pure conjunctive concepts), and to an algorithm that combines empirical and explanation-based learning. The model is used to gain insight into the behavior of these algorithms on a series of problems. Finally, we evaluate how well the average case model performs when the training examples violate the assumptions of the model.

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Text

Pazzani and Sarrett. "A Framework for Average Case Analysis of Conjunctive Learning Algorithms." Machine Learning, 1992. doi:10.1007/BF00994111

Markdown

[Pazzani and Sarrett. "A Framework for Average Case Analysis of Conjunctive Learning Algorithms." Machine Learning, 1992.](https://mlanthology.org/mlj/1992/pazzani1992mlj-framework/) doi:10.1007/BF00994111

BibTeX

@article{pazzani1992mlj-framework,
  title     = {{A Framework for Average Case Analysis of Conjunctive Learning Algorithms}},
  author    = {Pazzani, Michael J. and Sarrett, Wendy},
  journal   = {Machine Learning},
  year      = {1992},
  pages     = {349-372},
  doi       = {10.1007/BF00994111},
  volume    = {9},
  url       = {https://mlanthology.org/mlj/1992/pazzani1992mlj-framework/}
}