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dc.contributor.authorLiu, Yangen
dc.contributor.authorMadden, Michael G.en
dc.identifier.citationOne-Class Support Vector Machine Calibration Using Particle Swarm Optimisation , Yang Liu and Michael G. Madden. Proceedings of AICS-2007: 18th Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, August 2007.en
dc.description.abstractAbstract. Population-based search methods such as evolutionary algorithms, shuffled complex algorithms, simulated annealing and ant colony search are increasingly used as automatic calibration methods for a wide range of numerical models. This paper proposes the use of particle swarm optimisation to calibrate the parameters a one-class support vector machine. This approach is developed and tested in the calibration of a one-class SVM, applied to several data sets. The results indicate that the proposed method is able to match or surpass the performance of a one-class SVM with parameters optimized using a standard grid search method, with much lower CPU time required.en
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.subjectSupport vector machinesen
dc.subject.lcshSupport vector machinesen
dc.titleOne-Class Support Vector Machine Calibration Using Particle Swarm Optimisationen
dc.typeConference Paperen

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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland