Cristina Cornelio, Judy Goldsmith, et al.
JAIR
This report investigates the behavior of the a posteriori probabilities for classification problems in which the observations are not identically distributed. Some basic properties of the a posteriori probabilities are presented; then, it is shown that for each class the a posteriori probability converges a.s. to a random variable. Conditions are given for a.s. convergence of the a posteriori probability to 1 for the true class (and to 0 for all other classes). The results are illustrated for the case of two classes and binary observations, and finally a numerical example is presented. © 1977.
Cristina Cornelio, Judy Goldsmith, et al.
JAIR
Paul G. Comba
Journal of the ACM
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Guojing Cong, David A. Bader
Journal of Parallel and Distributed Computing