BAYESIAN CLASSIFICATION OF HIGH DIMENSIONAL DATA WITH GAUSSIAN PROCESS USING DIFFERENT KERNELS

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dc.contributor.authorOloyede, Isiaka
dc.date.accessioned2019-10-15T11:30:25Z
dc.date.available2019-10-15T11:30:25Z
dc.date.issued2018
dc.descriptionGaussian processes attempt to use mean and covariance function in lieu of mean and covariance used in Gaussian distribution. Though support vector machine is a celebrated classifier (SVM), it is not specifically designed to select features relevant to the predictor.en_US
dc.description.abstractThe study investigates asymptotic classification of high dimensional data by adopting Gaussian Process, five different kernels(covariance functions) were employed and compared to showcase the outperformed kernel asymptotically. Log marginal likelihood, Accuracy and log loss were the measurement criteria adopted to measure classification performances. The study therefore observed that the classification performed well asymptotically and found out that Gaussian Process Maximum Likelihood(GPML) had overall best model improvement asymptotically and across the covariance structures. K3 and K4 had the best accuracy in classification paradigm at the lower sample sizes but GPML and learned kernel had best model accuracy as the sample sizes tend to large sizes.en_US
dc.description.sponsorshipselfen_US
dc.identifier.citationOloyede.I (2018); BAYESIAN CLASSIFICATION OF HIGH DIMENSIONAL DATA WITH GAUSSIAN PROCESS USING DIFFERENT KERNELS, Anale. Seria Informatică. Vol. XVI fasc. 1 – 2018 Annals. Computer Science Series. 16th Tome 1st Fasc. – 2018en_US
dc.identifier.urihttp://hdl.handle.net/123456789/2556
dc.language.isoenen_US
dc.publisherAnale. Seria Informatică. Annals. Computer Science Series.en_US
dc.subjectBayesian, Kernelsen_US
dc.subjectClassificationen_US
dc.subjectGaussian Process.en_US
dc.titleBAYESIAN CLASSIFICATION OF HIGH DIMENSIONAL DATA WITH GAUSSIAN PROCESS USING DIFFERENT KERNELSen_US
dc.typeArticleen_US

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