An Empirical Model for Saturation and Capacity in Classifier Spaces

Research output: Chapter in Book/Report/Conference proceedingConference contribution


When assessing reported classification results based on selection of members from a database (e.g. a face database), one would like to know what an achievable classification rate is, given the noise level, dimensionality of the feature set and number of classes in the database. As best we can tell, no general results exist for this question, although many classification rates appear in different papers. This paper presents an empirical formula for MAP classification that links the number of discriminable classes to the error rate, dimensionality of the feature data and the feature noise level
Original languageEnglish
Title of host publicationPattern Recognition, 2006. ICPR 2006. 18th International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Print)0-7695-2521-0
Publication statusPublished - 2006


  • database theory
  • pattern classification
  • MAP classification
  • classification rate
  • classifier spaces
  • Convergence
  • Decision theory
  • Error analysis
  • Face detection
  • Information retrieval
  • Machine learning
  • Noise level
  • Pattern recognition
  • Probes
  • Spatial databases


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