  A useful method for representing Bayesian classifiers is through
\emph{discriminant functions}. Here, using copula functions, we propose a new
model for discriminants. This model provides a rich and generalized class of
decision boundaries. These decision boundaries significantly boost the
classification accuracy especially for high dimensional feature spaces. We
strengthen our analysis through simulation results.
