An Adaptive Kernel-based Bayesian Inference technique for failure classification

This paper outlines an Adaptive Kernel-based Bayesian Inference regression/classification technique that can be applied to a broad range of problems due to the scalable nature of the approach. In addition, the framework is built such that little manual adjustment of the classifier is needed when applying it to new problems thereby ensuring that the classifier can be readily applied to problems without time consuming customization. To test the performance of the framework it was applied to two very different classification problems; namely, a bearing health classification problem and a sonar image classification problem. The performance of the approach is very promising; however, further tests must be performed on larger data collections to truly gauge the overall scalability and performance.