Abstract :
An algorithm is presented for calculating recognition error when applying pattern vectors to an optimum Bayes´ classifier. The pattern vectors are assumed to come from two classes whose populations have Gaussian statistics with unequal covariance matrices and arbitrary a priori probabilities. The quadratic discriminant function associated with a Bayes´ classifier is used as a one-dimensional random variable from which the probability of error is calculated, once the distribution of the discriminant function is obtained.
Keywords :
Bayes´ optimum classifier, Bhattacharyya´s distance, characteristic function, divergence, multivariate Gaussian distributions, pattern recognition, recognition errors.; Approximation algorithms; Character recognition; Covariance matrix; Density functional theory; Gaussian distribution; Helium; Pattern recognition; Probability; Random variables; Statistical distributions; Bayes´ optimum classifier, Bhattacharyya´s distance, characteristic function, divergence, multivariate Gaussian distributions, pattern recognition, recognition errors.;
