The reduced Parzen classifier

The reduced Parzen classifier

The Parzen density estimate is known to be an effective tool for estimating the Bayes error, given a set of training samples from the class distributions. An algorithm is developed to select a given number of representative samples whose Parzen density estimate closely matches that of the entire sam...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 11; no. 4; pp. 423 - 425
Main Authors: Fukunaga, K., Hayes, R.R.
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01-04-1989
IEEE Computer Society
Subjects:
Algorithm design and analysis
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Covariance matrix
Design optimization
Error analysis
Exact sciences and technology
Gaussian distribution
Kernel
Millimeter wave radar
Pattern recognition
Pattern recognition. Digital image processing. Computational geometry
Probability distribution
Shape
Bayes estimation
Statistical analysis
Error estimation
Classification
Pattern recognition
Algorithm
Artificial intelligence
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Summary:The Parzen density estimate is known to be an effective tool for estimating the Bayes error, given a set of training samples from the class distributions. An algorithm is developed to select a given number of representative samples whose Parzen density estimate closely matches that of the entire sample set. Using this reduced representative set, a piecewise quadratic classifier which provides nearly optimal performance is designed.< >
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/34.19040