Performance and the Generalisation Error

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


Generalisation (test set) and empirical (training-set) classification errors are meaningful characteristics of any pattern classification system. Generally, one needs to know both these error rates and their relationship with the training-set sizes, the number of features, and the type of the classification rule. This knowledge can help one to choose a classifier of the proper complexity, with an optimal number of features, and to determine a sufficient number of training vectors. While training the non-linear SLP, one initially begins with the Euclidean distance classifier and then moves dynamically towards six increasingly complex statistical classifiers. Therefore, utilisation of theoretical generalisation error results obtained for these seven statistical classifiers becomes a guide for analysing the small sample properties of neural net generated classification algorithms.


Classification Error Generalisation Error Pattern Class Training Vector Asymptotic Error 
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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