GAUSSIAN PROCESS CLASSIFICATION AND SVM: MEAN FIELD RESULTS AND
LEAVE-ONE-OUT ESTIMATOR
Manfred Opper and Ole Winther
In this chapter, we elaborate on the well-known relationship between
Gaussian Processes (GP) and Support Vector Machines (SVM). Secondly,
we present approximate solutions for two computational problems arising
in GP and SVM. The first one is the calculation of the posterior mean
for GP classifiers using a `naive' mean field approach. The second one
is a leave-one-out estimator for the generalization error of SVM based
on a linear response method. Simulation results on a benchmark dataset
show similar performances for the GP mean field algorithm and the SVM
algorithm. The approximate leave-one-out estimator is found to be in
very good agreement with the exact leave-one-out error.
LU TP 99-06
To appear in Advances in Large Margin Classifiers, MIT Press, 1999.