Abstract
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
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
