Integration of Statistical and Neural Approaches

  • Šarūnas Raudys
Part of the Advances in Pattern Recognition book series (ACVPR)


The sheer number of statistical and neural network-based classification methods induces the important and practical question: “Which classification method or methods are superior?” In this chapter we address the similarities and differences between statistical pattern classification methods and nonparametric (structural) neural network classification methods. Rather than focusing on the confrontation between these two classification paradigms, we focus on integrating them in order to utilise their particular strengths and properties and to diminish their shortcomings.


Statistical Classifier Classification Rule Generalisation Error Distribution Density Function Neural Classifier 
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 London Limited 2001

Authors and Affiliations

  • Šarūnas Raudys
    • 1
  1. 1.Data Analysis DepartmentInstitute of Mathematics and InformaticsVilniusLithuania

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