References
- Cristianini, N. & Shawe-Taylor, N.J. 2000, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge.
- He, L. M., Kong, F. S. & Shen, Z. Q. 2005, “Multiclass SVM based on land cover classification with multisource data”, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, pp. 3541–3545.
- Hsu, C. W. & Lin, C. J. 2002, “A comparison of methods for multiclass support vector machines”, IEEE Transactions on Neural Network, Vol. 13, No. 2, pp. 415–425.
- Jardine, A. K. S., Lin, D. & Banjevic, D. 2006, “A review on machinery diagnostics and prognostics implementing condition-based maintenance”, Mechanical System and Signal Processing, Vol. 20, pp. 1483–1510.
- Knerr, S., Personnaz, L. & Dreyfus, G. 1990, “Single-layer learning revisited: a stepwise procedure for building and training a neural network”, Neurocomputing: Algorithms, Architectures and Applications, Fogelman, J. (editor), NATO ASI, Springer-Verlag, Berlin, pp. 41–50.
- Lee, J., Ni, J., Djurdjanovic, D., Qiu, H. & Liao, H. 2006, “Intelligent prognostics tools and e-maintenance”, Computers in Industry, Vol. 57, pp. 476–489.
- Li, Y., Billington, S., Zhang, C., Kurfess, T., Danyluk, S. & Liang, S. 1999, “Adaptive Prognostics for Rolling Element Bearing Condition”, Mechanical Systems and Signal Processing, Vol. 13, pp. 103–113.
- Liu, J., Djurdjanovic, D., Ni, J., Casoetto, N. & Lee, J. 2007, “Similarity based method for manufacturing process performance prediction and diagnosis”, Computers in Industry, Vol. 58, pp. 558–566.
- Pal, M. & Mather, P. M. 2004, “Assessment of the effectiveness of support vector machines for hyperspectral data”, Future Generation Computer Systems, Vol. 20, pp. 1215–1225.
- Platt, J. 1999, “Fast training of support vector machines using sequential minimal optimization”, Advances in Kernel Methods-Support Vector Learning, MIT Press, Cambridge.
- Niu, G., Han, T., Yang, B. S. & Tan, A. C. C. 2007a, “Multi-agent decision fusion for motor fault diagnosis”, Mechanical Systems and Signal Processing, Vol. 21.
- Niu, G., Son, J. D., Widodo, A., Yang, B. S., Hwang, D. H. & Kang, D. S. 2007b, “A comparison of classifier performance for fault diagnosis of induction motor using multi-type signals”, Technical Note of Structural Health Monitoring, Vol. 6, pp. 215–229.
- Vapnik, V. N. 1995, The Nature of Statistical Learning Theory, Springer-Verlag, New York.
- Vapnik, V. N. 1999, “An overview of statistical learning theory”, IEEE Transactions on Neural Networks, Vol. 10, No. 5, pp. 988–999.
- Weizhong, Y. & Feng, X. 2008, “Jet engine gas path fault diagnosis using dynamic fusion of multiple classifiers”, IEEE International Joint Conference on Neural Networks (IJCNN 2008), IEEE World Congress on Computational Intelligence, pp. 1585–1591.