Alain Vaucher, Philippe Schwaller, et al.
AMLD EPFL 2022
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.
Alain Vaucher, Philippe Schwaller, et al.
AMLD EPFL 2022
Yi Zhou, Parikshit Ram, et al.
ICLR 2023
Arthur Nádas
IEEE Transactions on Neural Networks
Corey Liam Lammie, Yotam Perlitz, et al.
VLDB 2026