Literature DB >> 8130520

An evaluation of factors influencing Bayesian learning systems.

E L Eisenstein1, F Alemi.   

Abstract

This paper examines the influences of situational and model factors upon the accuracy of Bayesian learning systems. In particular, it is concerned with the impact of variations in training sample size, number of attributes, choice of Bayesian model, and criteria for excluding model attributes upon the overall accuracy of the simple and proper Bayes models.

Mesh：

Year:  1993        PMID： 8130520      PMCID： PMC2850625     

Source DB:  PubMed          Journal:  Proc Annu Symp Comput Appl Med Care        ISSN： 0195-4210


  10 in total

1.  Diagnosis. I. Symptom nonindependence in mathematical models for diagnosis.

Authors:  M J Norusis; J A Jacquez
Journal:  Comput Biomed Res       Date:  1975-04

2.  Initial evaluation of a subjective Bayesian diagnostic system.

Authors:  D H Gustafson; J J Kestly; J H Greist; N M Jensen
Journal:  Health Serv Res       Date:  1971       Impact factor: 3.402

3.  APACHE II: a severity of disease classification system.

Authors:  W A Knaus; E A Draper; D P Wagner; J E Zimmerman
Journal:  Crit Care Med       Date:  1985-10       Impact factor: 7.598

4.  Self-learning for a Bayesian knowledge base: how long does it take for the machine to educate itself?

Authors:  T Chard
Journal:  Methods Inf Med       Date:  1987-10       Impact factor: 2.176

5.  Predicting in-hospital survival of myocardial infarction. A comparative study of various severity measures.

Authors:  F Alemi; J Rice; R Hankins
Journal:  Med Care       Date:  1990-09       Impact factor: 2.983

6.  The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Authors:  J A Hanley; B J McNeil
Journal:  Radiology       Date:  1982-04       Impact factor: 11.105

7.  Statistical approaches to the analysis of receiver operating characteristic (ROC) curves.

Authors:  B J McNeil; J A Hanley
Journal:  Med Decis Making       Date:  1984       Impact factor: 2.583

8.  Bayes' theorem and conditional nonindependence of data in medical diagnosis.

Authors:  D G Fryback
Journal:  Comput Biomed Res       Date:  1978-10-05

9.  Bayesian diagnostic probabilities without assuming independence of symptoms.

Authors:  A Gammerman; A R Thatcher
Journal:  Methods Inf Med       Date:  1991       Impact factor: 2.176

10.  Uncertainty/information as measure of various urographic parameters: an information theory model of diagnosis of renal masses.

Authors:  L R Bigongiari; D F Preston; L Cook; S J Dwyer; S Fritz; D G Fryback; J R Thornbury
Journal:  Invest Radiol       Date:  1981 Jan-Feb       Impact factor: 6.016
  10 in total

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