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Learning Bayesian-Neural Network from Mixed-Mode Data

  • Wang Limin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)

Abstract

For reasoning with uncertain knowledge the use of probability theory has been broadly investigated. This paper proposed a novel probabilistic network named Bayesian-Neural Network (BNN). BNN reduces computational complexity by dividing input attribute set into two parts, each modelled by Bayesian network or Neural network. The outputs produced by different classifiers is then solved in the output space by estimating the class-conditional structural mixtures. Empirical studies on a set of natural domains show that BNN has clear advantages with respect to the generalization ability.

Keywords

Bayesian Network Continuous Attribute Probabilistic Neural Network Attribute Node Probabilistic Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wang Limin
    • 1
  1. 1.Key Laboratary of Symbol Computation and Knowledge Engineering of Ministry, of EducationJiLin UniversityChangChunChina

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