A Rational Analysis of Categorization
Abstract
A rational analysis tries to predict the behavior of a cognitive system from the assumption it is optimized to the environment. An iterative categorization algorithm has been developed which attempts to get optimal Bayesian estimates of the probabilities that objects will display various features. A prior probability is estimated that an object comes from a category and combined with conditional probabilities of displaying features if the object comes from the category. Separate Bayesian treatments are offered for the cases of discrete and continuous dimensions. The resulting algorithm is efficient, works well in the case of large data bases, and replicates the full range of empirical literature in human categorization.
Cite
Text
Anderson and Matessa. "A Rational Analysis of Categorization." International Conference on Machine Learning, 1990. doi:10.1016/B978-1-55860-141-3.50013-4Markdown
[Anderson and Matessa. "A Rational Analysis of Categorization." International Conference on Machine Learning, 1990.](https://mlanthology.org/icml/1990/anderson1990icml-rational/) doi:10.1016/B978-1-55860-141-3.50013-4BibTeX
@inproceedings{anderson1990icml-rational,
title = {{A Rational Analysis of Categorization}},
author = {Anderson, John R. and Matessa, Michael},
booktitle = {International Conference on Machine Learning},
year = {1990},
pages = {76-84},
doi = {10.1016/B978-1-55860-141-3.50013-4},
url = {https://mlanthology.org/icml/1990/anderson1990icml-rational/}
}