ML Anthology

Bayesian Classification with Correlation and Inheritance

Robin Hanson, John C. Stutz, Peter C. Cheeseman
IJCAI 1991 pp. 692-698
/ijcai/1991/hanson1991ijcai-bayesian/

Abstract

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass system searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. Simpler versions of AutoClass have been applied to many large real data sets, have discovered new independently-verified phenomena, and have been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters though a class hierarchy. 1

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Text

Hanson et al. "Bayesian Classification with Correlation and Inheritance." International Joint Conference on Artificial Intelligence, 1991.

Markdown

[Hanson et al. "Bayesian Classification with Correlation and Inheritance." International Joint Conference on Artificial Intelligence, 1991.](https://mlanthology.org/ijcai/1991/hanson1991ijcai-bayesian/)

BibTeX

@inproceedings{hanson1991ijcai-bayesian,
  title     = {{Bayesian Classification with Correlation and Inheritance}},
  author    = {Hanson, Robin and Stutz, John C. and Cheeseman, Peter C.},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {1991},
  pages     = {692-698},
  url       = {https://mlanthology.org/ijcai/1991/hanson1991ijcai-bayesian/}
}