References
- Choi, Y. S. (2009). Least squares one-class support vector machine. Pattern Recognition Letters, 30, 1236–1240.
- Cortes, C., & Vapnik, V. (1995). Support-vector network. Machine Learning, 20, 273–297.
- de Kruif, B. J., & de Vries, T. J. A. (2003). Pruning error minimization in least squares support vector machines. IEEE Transactions on Neural Networks, 14(3), 696–702.
- Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical LASSO. Biostatistics, 9, 432–441.
- Kailath, T. (1980). Linear systems. Englewood Cliffs, NJ: Prentice & Hall.
- Kampmann, G., & Nelles, O. (2014). One-class LS-SVM with zero leave-one-out error. 2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA), Orlando, FL.
- Kuh, A., & De Wilde, P. (2007). Comments on ‘pruning error minimization in least squares support vector machines’. IEEE Transactions on Neural Networks, 18(2), 606–609.
- Kumar, S., Choudhary, A. K., Kumar, M., Shankar, R., & Tiwari, M. K. (2006). Kernel distance-based robust support vector methods and its application in developing a robust K-chart. International Journal of Production Research, 44(1), 77–96.
- Laskov, P., Gehl, C., Kruger, S., & Muller, K.-R. (2006). Incremental support vector learning: Analysis, implementation and applications. Journal of Machine Learning Research, 7, 1909–1936.
- Li, B., Wang, K., & Yeh, A. B. (2013). Monitoring covariance matrix via penalized likelihood estimation. IIE Transactions, 45, 132–146.
- Maboudou-Tchao, E. M. (2017). Kernel methods for changes detection in covariance matrices. Communications in Statistics - Simulation and Computation. doi:10.1080/03610918.2017.1322701
- Maboudou-Tchao, E. M., & Agboto, V. (2013). Monitoring the covariance matrix with fewer observations than variables. Computational Statistics & Data Analysis, 64, 99–112.
- Maboudou-Tchao, E. M., & Diawara, N. (2013). A lasso chart for monitoring the covariance matrix. Quality Technology and Quantitative Management, 10, 95–114.
- Maboudou-Tchao, E. M., Silva, I., & Diawara, N. (2016). Monitoring the mean vector with Mahalanobis kernels. Quality Technology and Quantitative Management (QTQM). doi:10.1080/16843703.2016.1226707
- Scholkopf, B., Platt, J. C., Shawe-Taylor, J., & Smola, A. J. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.
- Sun, R., & Tsung, F. A. (2003). Kernel-distance-based multivariate control charts using support vector methods. International Journal of Production Research, 41, 2975–2989.
- Suykens, J. A. K., & Vanderwalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9, 293–300.
- Tax, D., & Duin, R. (1999). Support vector domain description. Pattern Recognition Letters, 20, 1191–1199.
- Wang, K., Yeh, A. B., & Li, B. (2014). Simultaneous monitoring of process mean vector and covariance matrix via penalized likelihood estimation. Computational Statistics & Data Analysis, 78, 206–217.
- Yeh, A. B., Li, B., & Wang, K. (2012). Monitoring multivariate process variability with individual observations via penalized likelihood estimation. International Journal of Production Research, 50, 6624–6638.