On Parzen windows classifiers

Jing Peng, Guna Seetharaman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Parzen Windows classifiers have been applied to a variety of density estimation as well as classification tasks with considerable success. Parzen Windows are known to converge in the asymptotic limit. However, there is a lack of theoretical analysis on their performance with finite samples. In this paper we show a connection between Parzen Windows and the regularized least squares algorithm, which has a well-established foundation in computational learning theory. This connection allows us to provide useful insight into Parzen Windows classifiers and their performance in finite sample settings. Finally, we show empirical results on the performance of Parzen Windows classifiers using a number of real data sets.

Original languageEnglish
Title of host publication2014 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479959211
StatePublished - 12 Feb 2015
Event2014 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2014 - Washington, United States
Duration: 14 Oct 201416 Oct 2014

Publication series

NameProceedings - Applied Imagery Pattern Recognition Workshop
ISSN (Print)2164-2516


Other2014 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2014
Country/TerritoryUnited States


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