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
DOI10.1016/j.ifacol.2017.08.633
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
References
[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
Identifierhttps://irepository.cuhk.edu.cn/handle/3EPUXD0A/707
CollectionSchool of Data Science
Affiliation
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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