This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Vapnik, Vladimir N. The Nature of Statistical Learning Theory [M]// The nature of statistical learning theory /. Springer, 2000:988 - 999.10.1109/72.78864018252602Search in Google Scholar
Zhang X, Zhou J, Wang C, et al. Multi-class support vector machine optimized by intercluster distance and self-adaptive deferential evolution. Applied Mathematics & Computation, 2012, 218(9):4973-4987.10.1016/j.amc.2011.10.063Search in Google Scholar
Shen Z, Chen X, Zhang X, et al. A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM Measurement, 2012, 45(1):30-40.10.1016/j.measurement.2011.10.008Search in Google Scholar
Chen Z, Li J, Wei L. A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue. Artificial Intelligence in Medicine, 2007, 41(2):161-175.10.1016/j.artmed.2007.07.00817851055Search in Google Scholar
Widodo A, Yang B S. Wavelet support vector machine for induction machine fault diagnosis based on transient current signal. Expert Systems with Applications, 2008, 35(1-2):307-316.10.1016/j.eswa.2007.06.018Search in Google Scholar
Xiang X, Zhou J, An X, et al. Fault diagnosis based on Walsh transforms and support vector machine. Mechanical Systems & Signal Processing, 2008, 22(7):1685-1693.10.1016/j.ymssp.2008.01.005Search in Google Scholar
Gao X, Liu F. Robust visual tracking based on support vector machine and weighted sampling method. International Journal on Smart Sensing & Intelligent Systems, 2015, 8(1):255-271.10.21307/ijssis-2017-758Search in Google Scholar
Zhang K, Li Y, Scarf P, et al. Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks Neurocomputing, 2011, 74(17):2941-2952.10.1016/j.neucom.2011.03.043Search in Google Scholar
Anne E, Alip B, Stephen B, et al. Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm-support vector machines: HGASVM. Expert Systems with Applications, 2009, 36(2):1391-1402.10.1016/j.eswa.2007.11.014Search in Google Scholar
Lin S W, Lee Z J, Chen S C, et al. Parameter determination of support vector machine and feature selection using simulated annealing approach Applied Soft Computing, 2008, 8(4):1505- 1512.10.1016/j.asoc.2007.10.012Search in Google Scholar
Huang C L. ACO-based hybrid classification system with feature subset selection and model parameters optimization Neurocomputing, 2009, 73(1–3):438-448.10.1016/j.neucom.2009.07.014Search in Google Scholar
Widiyanto D, Purnomo D M J, Jati G, et al. Modification of particle swarm optimization by reforming global best term to accelerate the searching of odor sources. International Journal on Smart Sensing & Intelligent Systems, 2016, 9(3):1412-1430.10.21307/ijssis-2017-924Search in Google Scholar
Karaboga D, Basturk B. On the performance of artificial bee colony (ABC) algorithm Applied Soft Computing, 2008, 8(1):687-697.10.1016/j.asoc.2007.05.007Search in Google Scholar
Karaboga D, Akay B. A comparative study of Artificial Bee Colony algorithm Applied Mathematics & Computation, 2009, 214(1):108-132.10.1016/j.amc.2009.03.090Search in Google Scholar
Lei Y, He Z, Zi Y. A new approach to intelligent fault diagnosis of rotating machinery Expert Systems with Applications, 2008, 35(4):1593-1600.10.1016/j.eswa.2007.08.072Search in Google Scholar
Hsu C W, Lin C J. Errata to “A comparison of methods for multiclass support vector machines”.. IEEE Transactions on Neural Networks, 2002, 13(4):415-425.Search in Google Scholar
Xu C, Duan H, Liu F. Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planning. Aerospace Science & Technology, 2010, 14(8):535-541.10.1016/j.ast.2010.04.008Search in Google Scholar
C.-W. Hsu, C.-C. Chang, C.-J. Lin. “A Practical Guide to Support Vector Classification”, <http://www.csie.ntu.edu.tw/_cjlin/libsvm/>, 2009.Search in Google Scholar
K. A. Loparo, “Bearings Vibration Data Set”, Case Western Reserve University [EB/OL]<http://www.eecs.case.edu/laboratory/bearing/welcome_overview.htm>Search in Google Scholar