An Empirical Model for Saturation and Capacity in Classifier Spaces

When assessing reported classification results based on selection of members from a database (e.g. a face database), one would like to know what is an achievable classification rate, 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.

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Robert B. Fisher
• Keywords: Achievable Classification Rate, Classification Rates, Computer Vision, Feature Noise Level, ICPR 2006