DocumentCode
900740
Title
Weighted Parzen windows for pattern classification
Author
Babich, Gregory A. ; Camps, Octavia I.
Author_Institution
Appl. Res. Lab., Pennsylvania State Univ., University Park, PA, USA
Volume
18
Issue
5
fYear
1996
fDate
5/1/1996 12:00:00 AM
Firstpage
567
Lastpage
570
Abstract
This paper introduces the weighted-Parzen-window classifier. The proposed technique uses a clustering procedure to find a set of reference vectors and weights which are used to approximate the Parzen-window (kernel-estimator) classifier. The weighted-Parzen-window classifier requires less computation and storage than the full Parzen-window classifier. Experimental results showed that significant savings could be achieved with only minimal, if any, error rate degradation for synthetic and real data sets
Keywords
approximation theory; estimation theory; image classification; probability; Bayes error; clustering; discriminant analysis; error rate degradation; nonparametric classifiers; pattern classification; training samples; weighted Parzen windows; Degradation; Density functional theory; Entropy; Error analysis; Kernel; Monitoring; Pattern analysis; Pattern classification; Pattern recognition; Training data;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.494647
Filename
494647
Link To Document