@ARTICLE{Cui_Jiang_Analog_2011, author={Cui, Jiang and Wang, Youren}, number={No 4}, journal={Metrology and Measurement Systems}, pages={569-582}, howpublished={online}, year={2011}, publisher={Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation}, abstract={This paper presents a novel strategy of fault classification for the analog circuit under test (CUT). The proposed classification strategy is implemented with the one-against-one Support Vector Machines Classifier (SVC), which is improved by employing a fault dictionary to accelerate the testing procedure. In our investigations, the support vectors and other relevant parameters are obtained by training the standard binary support vector machines. In addition, a technique of radial-basis-function (RBF) kernel parameter evaluation and selection is invented. This technique can find a good and proper kernel parameter for the SVC prior to the machine learning. Two typical analog circuits are demonstrated to validate the effectiveness of the proposed method.}, type={Artykuły / Articles}, title={Analog Circuit Fault Classification Using Improved One-Against-One Support Vector Machines}, URL={http://www.czasopisma.pan.pl/Content/89821/PDF/Journal10178-VolumeXVIII%20Issue4_05paper.pdf}, doi={10.2478/v10178-011-0055-7}, keywords={analog circuit, fault classification, Support Vector Machines classifier, fault dictionary, kernel parameter}, }