Learning Vector Quantization Classification with Local Relevance Determination for Medical Data

  • B. Hammer
  • T. Villmann
  • F. -M. Schleif
  • C. Albani
  • W. Hermann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


In this article we extend the global relevance learning vector quantization approach by local metric adaptation to obtain a locally optimized model for classification. In this sense we make a step in the direction of quadratic discriminance analysis in statistics where classwise variance matrices are used for class adapted discriminance functions. We demonstrateb the performance of the model for a medical application.


Cost Function Generalization Ability Learn Vector Quantization Generalization Bound Generalize Learn Vector Quantization 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Albani, C., Blaser, G., Rietz, U., Villmann, T., Geyer, M.: Die geschlechtsspezifische Erfassung körperlicher Beschwerden bei PsychotherapiepatientInnen mit dem ”Gießener Beschwerdebogen” (GBB). In: Hinz, A., Decker, O. (eds.) Gesundheit im gesellschaftlichen Wandel, Psychosozial-Verlag, Gießen (2006)Google Scholar
  2. 2.
    Bartlett, P.L., Mendelson, S.: Rademacher and Gaussian complexities: risk bounds and structural results. Journal of Machine Learning and Research 3, 463–482 (2002)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Franke, G.H.: Möglichkeiten und Grenzen im Einsatz der Symptom-Check-List-90-R. Verhaltenstherapie & psychosoziale Praxis 33, 475–485 (2001)Google Scholar
  4. 4.
    Hammer, B., Strickert, M., Villmann, T.: Supervised neural gas with general similarity measure. Neural Processing Letters 21, 21–44 (2005)CrossRefGoogle Scholar
  5. 5.
    Hammer, B., Strickert, M., Villmann, T.: On the generalization ability of GRLVQ networks. Neural Processing Letters 21, 109–120 (2005)CrossRefGoogle Scholar
  6. 6.
    Hammer, B., Villmann, T.: On the generalization ability of localized GRLVQ. Technical Report Clausthal University of Technology, Institute for Computer Science (2005)Google Scholar
  7. 7.
    Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1997)zbMATHGoogle Scholar
  8. 8.
    Sato, A.S., Yamada, K.: Generalized learning vector quantization. In: Tesauro, G., Touretzky, D., Leen, T. (eds.) Advances in Neural Information Processing Systems, vol. 7, pp. 423–429. MIT Press, Cambridge (1995)Google Scholar
  9. 9.
    Villmann, T.: Neural Maps for Faithful Data Modelling in Medicine - State of the Art and Exemplary Applications. Neurocomputing 48, 229–250 (2002)zbMATHCrossRefGoogle Scholar
  10. 10.
    Villmann, T., Blaser, G., Körner, A., Albani, C.: Relevanzlernen und statistische Diskriminanzverfahren zur ICD-10 Klassifizierung von SCL90-Patienten-Profilen bei Therapiebeginn. In: G. Plöttner (ed.). Psychotherapeutische Versorgung und Versorgungsforschung. Leipziger Universitätsverlag, Leipzig (2004)Google Scholar
  11. 11.
    Vapnik, V., Chervonenkis, A.: On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications 16(2), 264–280 (1971)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • B. Hammer
    • 1
  • T. Villmann
    • 2
  • F. -M. Schleif
    • 3
  • C. Albani
    • 2
  • W. Hermann
    • 4
  1. 1.Institute of Computer ScienceClausthal University of TechnologyClausthal-ZellerfeldGermany
  2. 2.Clinic for PsychotherapyUniversity of LeipzigLeipzigGermany
  3. 3.Institute of Computer ScienceUniversity of LeipzigGermany
  4. 4.Paracelsus Hospital ZwickauGermany

Personalised recommendations