BAYESIAN CLASSIFICATION OF HIGH DIMENSIONAL DATA WITH GAUSSIAN PROCESS USING DIFFERENT KERNELS
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Date
2018
Authors
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Publisher
Anale. Seria Informatică. Annals. Computer Science Series.
Abstract
The 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.
Description
Gaussian 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.
Keywords
Bayesian, Kernels, Classification, Gaussian Process.
Citation
Oloyede.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. – 2018