  We investigate the performance of a simple signed distance function (SDF)
based method by direct comparison with standard SVM packages, as well as
K-nearest neighbor and RBFN methods. We present experimental results comparing
the SDF approach with other classifiers on both synthetic geometric problems
and five benchmark clinical microarray data sets. On both geometric problems
and microarray data sets, the non-optimized SDF based classifiers perform just
as well or slightly better than well-developed, standard SVM methods. These
results demonstrate the potential accuracy of SDF-based methods on some types
of problems.

Machine Learning;Computational Geometry