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

Comparison of Binary Classification Based on Signed Distance Functions with Support Vector Machines