ML Anthology

Factorization in Experiment Generation

Devika Subramanian, Joan Feigenbaum
AAAI 1986 pp. 518-522
/aaai/1986/subramanian1986aaai-factorization/

Abstract

Experiment generation is an important part of incremental concept learning. One basic function of experimentation is to gather data to refine the existing space of hypotheses[DB83]. Here we examine the class of experiments that accomplish this, called discrimination experiments, and propose factoring as a technique for generating them efficiently.

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Text

Subramanian and Feigenbaum. "Factorization in Experiment Generation." AAAI Conference on Artificial Intelligence, 1986.

Markdown

[Subramanian and Feigenbaum. "Factorization in Experiment Generation." AAAI Conference on Artificial Intelligence, 1986.](https://mlanthology.org/aaai/1986/subramanian1986aaai-factorization/)

BibTeX

@inproceedings{subramanian1986aaai-factorization,
  title     = {{Factorization in Experiment Generation}},
  author    = {Subramanian, Devika and Feigenbaum, Joan},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {1986},
  pages     = {518-522},
  url       = {https://mlanthology.org/aaai/1986/subramanian1986aaai-factorization/}
}