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Hyperparameter Selection for Gaussian Process One-Class Classification
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DOI：10.1109/TNNLS.2014.2363457.png)
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Abstract
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Gaussian processes (GPs) provide predicted outputs with a full conditional statistical description, which can be used to establish confidence intervals and to set hyperparameters. This characteristic provides GPs with competitive or better performance in various applications. However, the specificity of one-class classification (OCC) makes GPs unable to select suitable hyperparameters in their traditional way. This brief proposes to select hyperparameters for GP OCC using the prediction difference between edge and interior positive training samples. Experiments on 2-D artificial and University of California benchmark data sets verify the effectiveness of this method.
Keywords:
Covariance function
Gaussian processes (GPs)
hyperparameter selection
one-class classification (OCC)
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