  We consider the minimum error entropy (MEE) criterion and an empirical risk
minimization learning algorithm in a regression setting. A learning theory
approach is presented for this MEE algorithm and explicit error bounds are
provided in terms of the approximation ability and capacity of the involved
hypothesis space when the MEE scaling parameter is large. Novel asymptotic
analysis is conducted for the generalization error associated with Renyi's
entropy and a Parzen window function, to overcome technical difficulties arisen
from the essential differences between the classical least squares problems and
the MEE setting. A semi-norm and the involved symmetrized least squares error
are introduced, which is related to some ranking algorithms.
