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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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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.
Keyword:
Covariance function
Gaussian processes (GPs)
hyperparameter selection
one-class classification (OCC)
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Two methods of selecting Gaussian kernel parameters for one-class SVM and their application to fault detection一类支持向量机的两种高斯核参数选择方法及其在故障检测中的应用
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