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

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dc.creatorBoczko, Erik M.
dc.creatorYoung, Todd
dc.creatorZie, Minhui
dc.creatorWu, Di
dc.date2008-12-16
dc.date.accessioned2026-07-07T12:13:23Z
dc.date.available2026-07-07T12:13:23Z
dc.descriptionWe 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.
dc.description5 pages, 4 figures. Presented at the Ohio Collaborative Conference on Bioinformatics (OCCBIO), June 2006
dc.identifierhttps://arxiv.org/abs/0812.3147
dc.identifierhttp://arxiv.org/abs/0812.3147
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/210830
dc.subjectMachine Learning
dc.subjectComputational Geometry
dc.titleComparison of Binary Classification Based on Signed Distance Functions with Support Vector Machines
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