  This paper considers the epistemic justification for a simplicity preference
in inductive inference that may be obtained from the machine learning framework
of statistical learning theory. Uniting elements from both earlier arguments
suggesting and rejecting such a justification, the paper spells out a qualified
means-ends and model-relative justificatory argument, built on statistical
learning theory's central mathematical learning guarantee for the method of
empirical risk minimization.
