Details of Research Outputs

TitleTuning of Hyperparameters for FIR models – an Asymptotic Theory
Author (Name in English or Pinyin)
Mu, B.1; Chen, T.1,2; Ljung, L.1
Date Issued2017-07-01
Source PublicationIFAC-PapersOnLine
Firstlevel Discipline信息科学与系统科学
Education discipline科技类
Published range国外学术期刊
Volume Issue Pages50(1), 2818-2823
[1] Amemiya, T., Advanced Economentrics, 1985, Harvard University Press.
[2] Aravkin, A., Burke, J., Chiuso, A., and Pillonetto, G. (2012). On the mse properties of empirical bayes methods for sparse estimation. In Proceeding of the IFAC Symposium on System Identification. Brussels, Belgium.
[3] Aravkin, A., Burke, J., Chiuso, A., Pillonetto, G., Convex vs non-convex estimators for regression and sparse estimation: the mean squared error properties of ard and glasso. Journal of Machine Learning Research 15 (2014), 217–252.
[4] Chen, T., Andersen, M., Ljung, L., Chiuso, A., Pil-lonetto, G., System identification via sparse multiple kernel-based regularization using sequential convex optimization techniques. Automatic Control, IEEE Transactions on 59:11 (2014), 2933–2945.
[5] Chen, T., Ohlsson, H., Goodwin, G., and Ljung, L. (2011). Kernel selection in linear system identification. part ii: A classical perspective. In Proc 50th IEEE Congference on Decision and Control, CDC. Orlando, FL.
[6] Chen, T., Ohlsson, H., Ljung, L., On the estimation of transfer functions, regularizations and Gaussian processes-Revisited. Automatica 48:8 (2012), 1525–1535.
[7] Ljung, L., System Identification - Theory for the User, 2nd edition, 1999, Prentice-Hall, Upper Saddle River, N.J.
[8] Marconato, A., Ishteva, M., and Schoukens, J. (2015). On the performance of regularized FIR models for long data records. Automatica. Submitted.
[9] Pillonetto, G., Chiuso, A., Tuning complexity in regularized kernel-based regression and linear system identification: The robustness of the marginal likelihood. Automatica 58 (2015), 106–117.
[10] Pillonetto, G., Dinuzzo, F., Chen, T., De Nicolao, G., Ljung, L., Kernel methods in system identification, machine learning and function estimation: A survey. Automatica 50:3 (2014), 657–682 2014.
[11] Pillonetto, G., De Nicolao, G., A new kernel-based approach for linear system identification. Auto-matica 46:1 (2010), 81–93.
[12] Stein, C. (1981). Estimation of the mean of a multivariate normal distribution. Annals of Statistics, 1135–1151.
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionSchool of Data Science
1.Division of Automatic Control, Linköping University, Linköping, Sweden
2.School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China
Recommended Citation
GB/T 7714
Mu, B.,Chen, T.,Ljung, L. Tuning of Hyperparameters for FIR models – an Asymptotic Theory[J]. IFAC-PapersOnLine,2017.
APA Mu, B., Chen, T., & Ljung, L. (2017). Tuning of Hyperparameters for FIR models – an Asymptotic Theory. IFAC-PapersOnLine.
MLA Mu, B.,et al."Tuning of Hyperparameters for FIR models – an Asymptotic Theory".IFAC-PapersOnLine (2017).
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