TY - GEN
T1 - Searching optimal sigma parameter in Radial Basis Kernel Support Vector Machine for classification of HIV sub-type viruses
AU - Kurt, Zeyneb
AU - Yavuz, Oguzhan
PY - 2010
Y1 - 2010
N2 - We propose intelligent methods to classify two different HIV virus types, i.e., R5X4 and R5 or X4 with low computational complexity. Since R5X5 virus has same the features of R5 and X4 viruses, diagnosis of R5X4 can not be determined easily. In this study, the statistical data of R5X4, R5 and X4 was obtained by accessible residues and modelled by Auto-regressive (AR) model. After that the pre-processed data was used for determining the optimal σ value in Radial Basis Kernel of Support Vector Machine (SVM).
AB - We propose intelligent methods to classify two different HIV virus types, i.e., R5X4 and R5 or X4 with low computational complexity. Since R5X5 virus has same the features of R5 and X4 viruses, diagnosis of R5X4 can not be determined easily. In this study, the statistical data of R5X4, R5 and X4 was obtained by accessible residues and modelled by Auto-regressive (AR) model. After that the pre-processed data was used for determining the optimal σ value in Radial Basis Kernel of Support Vector Machine (SVM).
KW - Auto-regressive Model
KW - HTV
KW - ROC analysis
KW - Support Vector Machine
UR - http://www.scopus.com/inward/record.url?scp=78751542985&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:78751542985
SN - 9789898425195
T3 - SIGMAP 2010 - Proceedings of the International Conference on Signal Processing and Multimedia Applications
SP - 163
EP - 166
BT - SIGMAP 2010 - Proceedings of the International Conference on Signal Processing and Multimedia Applications
T2 - International Conference on Signal Processing and Multimedia Applications, SIGMAP 2010
Y2 - 26 July 2010 through 28 July 2010
ER -
