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Risk Estimation for Hierarchical Classifier

  • I. T. Podolak
  • A. Roman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6678)

Abstract

We describe the Hierarchical Classifier (HC), which is a hybrid architecture [1] built with the help of supervised training and unsupervised problem clustering. We prove a theorem giving the estimation \(\hat{R}\) of HC risk. The proof works because of an improved way of computing cluster weights, introduced in this paper. Experiments show that \(\hat{R}\) is correlated with HC real error. This allows us to use \(\hat{R}\) as the approximation of HC risk without evaluating HC subclusters. We also show how \(\hat{R}\) can be used in efficient clustering algorithms by comparing HC architectures with different methods of clustering.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • I. T. Podolak
    • 1
  • A. Roman
    • 1
  1. 1.Institute of Computer ScienceJagiellonian UniversityPoland

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