TY - JOUR N2 - 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. L1 - http://www.journals.pan.pl/Content/89821/PDF/Journal10178-VolumeXVIII%20Issue4_05paper.pdf L2 - http://www.journals.pan.pl/Content/89821 PY - 2011 IS - No 4 EP - 582 DO - 10.2478/v10178-011-0055-7 KW - analog circuit KW - fault classification KW - Support Vector Machines classifier KW - fault dictionary KW - kernel parameter A1 - Cui, Jiang A1 - Wang, Youren PB - Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation DA - 2011 T1 - Analog Circuit Fault Classification Using Improved One-Against-One Support Vector Machines SP - 569 UR - http://www.journals.pan.pl/dlibra/publication/edition/89821 T2 - Metrology and Measurement Systems ER -