Abstract：Gaussian process classifier based on Laplace approximation method (LGPC) is constructed. It can optimize the hyper parameters of the LGPC automatically, output classification results in probability, and be convenient to analyze problems’ uncertainty. Therefore, LGPC can overcome the inherent limitations of SVM whose regularization factors and kernel function parameters are difficult to determine. In this paper, performance of LGPC is analyzed and validated by typical classification datasets, and transformer fault diagnosing method based on LGPC is presented and described in details. Experimental results show that the diagnosing correctness ratios are higher when mean function adopts a constant function, covariance function adapts a full square exponential function and likelihood function adopts an error function. Compared with methods based on SVM, the proposed method has higher classification accuracy, which proves it is effective.
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2013, Vol. 28 
