By Topic

Asymptotic efficiency of classifying procedures using the Hermite series estimate of multivariate probability densities (Corresp.)

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)

Pattern recognition procedures derived from a nonparametric estimate of multivariate probability density functions using the orthogonal Hermite system are examined. For sufficiently regular densities, the convergence rate of the mean integrated square error (MISE) isO(n^{-l+epsilon}),epsilon >0, wherenis the number of observations and is independent of the dimension. As a consequence, the rate at which the probability of misclassification converges to the Bayes probability of error as the lengthnof the learning sequence tends to infinity is also independent of the dimension of the class densities and equalsO(n^{-1/2+ delta}), delta >O.

Published in:

Information Theory, IEEE Transactions on  (Volume:27 ,  Issue: 3 )