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The reduced Parzen classifier
April 1989 (vol. 11 no. 4)
pp. 423,424,425
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.<>

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Index Terms:
pattern recognition,Bayes methods,error analysis,estimation theory,piecewise quadratic classifier,pattern recognition,Parzen classifier,Parzen density estimate,Bayes error,representative samples,Gaussian distribution,Error analysis,Kernel,Shape,Millimeter wave radar,Pattern recognition,Covariance matrix,Probability distribution,Design optimization,Algorithm design and analysis
Citation:
"The reduced Parzen classifier," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 4, pp. 423,424,425, April 1989, doi:10.1109/34.19040
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