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Discrete Discriminant Analysis: The Performance of Combining Models by a Hierarchical Coupling Approach

  • Ana Sousa Ferreira
  • Gilles Celeux
  • Helena Bacelar-Nicolau
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

We are concerned with combining models in discrete discriminant analysis in the multiclass (K > 2) case. Our approach consists of decomposing the multiclass problem in several biclass problems embedded in a binary tree. The affinity coefficient (Matusita (1955); Bacelar-Nicolau (1981,1985)) is proposed for the choice of the hierarchical couples, at each level of the tree, among all possible forms of merging. For the combination of models we consider a single coefficient: a measure of the relative performance of models - the integrated likelihood coefficient (Ferreira et al., 1999)) and we evaluate its performance.

Keywords

Multiclass Problem Psychological Data Vocational Identity Conditional Probability Function Kernel Discriminant Analysis 
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 2000

Authors and Affiliations

  • Ana Sousa Ferreira
    • 1
  • Gilles Celeux
    • 2
  • Helena Bacelar-Nicolau
    • 1
  1. 1.LEAD, Faculdade de Psicologia e Ciências da EducaçãoAlameda da UniversidadeLisboaPortugal
  2. 2.INRIA-Rhône AlpesGrenobleFrance

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