Title :
On the reduction of the nearest-neighbor variation for more accurate classification and error estimates
Author :
Djouadi, Abdelhamid
Author_Institution :
Lucent Technol., Columbus, OH, USA
fDate :
5/1/1998 12:00:00 AM
Abstract :
In designing the nearest-neighbor (NN) classifier, a method is presented to produce a finite sample size risk close to the asymptotic one. It is based on an attempt to eliminate the first-order effects of the sample size, as well as all higher odd terms. This method uses the 2-NN rule without the rejection option and utilizes a polarization scheme. Simulation results are included as a means of verifying this analysis
Keywords :
Bayes methods; error analysis; estimation theory; minimisation; pattern classification; probability; Bayes errors; asymptotic risk; error estimation; minimisation; nearest-neighbor classifier; pattern classification; polarization; probability; risk estimation; Analytical models; Convergence; Density measurement; Neural networks; Pattern recognition; Polarization; Probability density function; Probability distribution; Size measurement; Weight measurement;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
