Bayesian Classification with Correlation and Inheritance
Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. Learning Be Knowledge Acquisition. Pages 692-697.
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.
@inproceedings{ijcai1991p101,
title = {Bayesian Classification with Correlation and Inheritance},
author = {Hanson, Robin and Stutz, John C. and Cheeseman, Peter C.},
booktitle = {Proceedings of the Twelfth International Joint Conference on
Artificial Intelligence, {IJCAI-91}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {John Mylopoulos and Ray Reiter},
pages = {692--697},
year = {1991},
month = {8},
note = {Learning Be Knowledge Acquisition},
url = {https://ijcai.org/proceedings/1991/101},
}
TY - CPAPER TI - Bayesian Classification with Correlation and Inheritance AU - Hanson, Robin AU - Stutz, John C. AU - Cheeseman, Peter C. T2 - Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, IJCAI-91 PB - International Joint Conferences on Artificial Intelligence PY - 1991 SP - 692 EP - 697 UR - https://ijcai.org/proceedings/1991/101 ER -Copied
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