  The imminent advent of very large-scale optical sky surveys, such as Euclid
and LSST, makes it important to find efficient ways of discovering rare objects
such as strong gravitational lens systems, where a background object is
multiply gravitationally imaged by a foreground mass. As well as finding the
lens systems, it is important to reject false positives due to intrinsic
structure in galaxies, and much work is in progress with machine learning
algorithms such as neural networks in order to achieve both these aims. We
present and discuss a Support Vector Machine (SVM) algorithm which makes use of
a Gabor filterbank in order to provide learning criteria for separation of
lenses and non-lenses, and demonstrate using blind challenges that under
certain circumstances it is a particularly efficient algorithm for rejecting
false positives. We compare the SVM engine with a large-scale human examination
of 100000 simulated lenses in a challenge dataset, and also apply the SVM
method to survey images from the Kilo-Degree Survey.
