Embedded Bayesian Network Classifiers

Embedded Bayesian Network Classifiers (1997)

Cached

Download Links

[pegasus.ece.utexas.edu]

by David Heckerman , Christopher Meek

Citations:

8 - 1 self

Versions

Version History

Metadata Version 3

User correction supplied by SystemCorrections

Datum	Value	Source
TITLE	Embedded Bayesian Network Classifiers	user correction - Legacy Corrections
AUTHOR NAME	David Heckerman	SVM HeaderParse 0.1
AUTHOR NAME	Christopher Meek	SVM HeaderParse 0.1
AUTHOR AFFIL	Microsoft Research; Advanced Technology Division; Microsoft Corporation	SVM HeaderParse 0.2
AUTHOR ADDR	One Microsoft Way; Redmond, WA	SVM HeaderParse 0.1
ABSTRACT	Low-dimensional probability models for local distribution functions in a Bayesian network include decision trees, decision graphs, and causal independence models. We describe a new probability model for discrete Bayesian networks, which we call an embedded Bayesian network classifier or EBNC. The model for a node Y given parents X is obtained from a (usually different) Bayesian network for Y and X in which X need not be the parents of Y . We show that an EBNC is a special case of a softmax polynomial regression model. Also, we show how to identify a non-redundant set of parameters for an EBNC, and describe an asymptotic approximation for learning the structure of Bayesian networks that contain EBNCs. Unlike the decision tree, decision graph, and causal independence models, we are unaware of a semantic justification for the use of these models. Experiments are needed to determine whether the models presented in this paper are useful in practice. Keywords: Bayesian networks, model dimen...	user correction - Legacy Corrections
YEAR	1997	INFERENCE
VENUE TYPE	TECHREPORT	INFERENCE
TECH	Technical Report MSR-TR-97-06, Microsoft Research	INFERENCE
CITATIONS	12 found	ParsCit 1.0