A comparison of Bayesian network learning algorithms from continuous data.

A comparison of Bayesian network learning algorithms from continuous data. Research Abstract Details 

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A comparison of Bayesian network learning algorithms from continuous data. Abstract Text:

Lawrence D Fu,Ioannis Tsamardinos,

Learning a Bayesian network from data is an important problem in biomedicine for the automatic construction of decision support systems and inference of plausible causal relations. Most Bayesian network learning algorithms require discrete data; however discretization may impact the quality of the learned structure. In this project, we present a comparison of different approaches for learning from continuous data to identify the most promising one and to quantify the impact of discretization in Bayesian network learning.

A comparison of Bayesian network learning algorithms from continuous data. Publishing Authors By Initials

LD Fu,I Tsamardinos,

For similar natural sciences: mathematics: mathematical computing: decision support techniques: neural networks (computer) research abstracts see: natural sciences: mathematics: mathematical computing: decision support techniques: neural networks (computer) research

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A comparison of Bayesian network learning algorithms from continuous data. Journal Published:

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: AMIA ... Annual Symposium proceedings / AMIA Sympo

VOLUME:

Page Numbers: 960

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ISSN: 1559-4076

DAY: 3

MONTH: 12

YEAR: 2005

A comparison of Bayesian network learning algorithms from continuous data. Information

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LANGUAGE: eng

NlmUniqueID: 101209213

A comparison of Bayesian network learning algorithms from continuous data. Keywords Mesh Terms:

KEYWORDS: Neural Networks (Computer)

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Chemical & Substance for Abstract: A comparison of Bayesian network learning algorithms from continuous data. Information

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Grant and Affiliation Information for A comparison of Bayesian network learning algorithms from continuous data.

AFFILIATION: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.

Country: United States

AGENCY: United States NLM

GRANT: T15 LM 007450-03

ACRONYM: LM

MEDLINETA: AMIA Annu Symp Proc

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