Suppose we use a big set of features
to ensure that the two classes are
linearly separable. What is the best
separating line to use?
The Bayesian answer is to use them
all (including ones that do not quite
separate the data.)
Weight each line by its posterior
probability (i.e. by a combination of
how well it fits the data and how well it
fits the prior).
Is there an efficient way to
approximate the correct Bayesian