University of Hertfordshire

By the same authors

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Original languageEnglish
Number of pages4
DOIs
Publication statusPublished - 26 Apr 2018
Event 2018 IEEE International Symposium on Circuits and Systems (ISCAS) - Florence, Italy
Duration: 27 May 2018 → …

Conference

Conference 2018 IEEE International Symposium on Circuits and Systems (ISCAS)
CountryItaly
CityFlorence
Period27/05/18 → …

Abstract

Except for the advantages of supporting arbitrary kernels, probabilistic predictions and automatic estimation of hyper-parameters, relevance vector machine (RVM) also encounters some of training time increase and classification accuracy recession, compared with SVM. In order to suppress such ‘nuisance’ imperfections, this paper proposed a sparsity-aware RVM model for multi-class classification (denoted as Sa-MRVM) by developing a configurable singular entropy decision mechanism. Multiple driven data sets captured from both emulational and actual circuits under test (CUTs) are involved to further improve the model's generalization ability and judging confidence. Experimental results carried out on two CUTs indicate that our proposed learning methodology is speedy and accurate enough for real world fault diagnosis tasks of analog and mixed-signal circuits.

ID: 15416981