The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A new kernel-based approach for linear system identification
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Cites
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Cited by
Kernel methods in system identification, machine learning and function estimation: A survey
Pillonetto, G., Dinuzzo, F., Chen, T., De Nicolao, G., & Ljung, L. (2014) · Automatica · doi:10.1016/j.automatica.2014.01.001 · Cited by 741
Self-citationOn the estimation of transfer functions, regularizations and Gaussian processes—Revisited
Chen, T., Ohlsson, H., & Ljung, L. (2012) · Automatica · doi:10.1016/j.automatica.2012.05.026 · Cited by 451
On asymptotic properties of hyperparameter estimators for kernel-based regularization methods
Mu, B., Chen, T., & Ljung, L. (2018) · Automatica · doi:10.1016/j.automatica.2018.04.035 · Cited by 80
Mu, B., Chen, T., & Ljung, L. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.09.130 · Cited by 32
Gray-box identification with regularized FIR models
Münker, T., Peter, T. J., & Nelles, O. (2018) · at - Automatisierungstechnik · doi:10.1515/auto-2018-0026 · Cited by 18
Metadata
- Authors
- Pillonetto, G., & De Nicolao, G.
- Venue
- Automatica
- Publisher
- Elsevier BV
- Published
- 2010-01
- Volume
- 46
- Issue
- 1
- Pages
- 81-93
- ISSN
- 0005-1098 (ISSN-L)
- OpenAlex
- W2021065610
- Cited by
- 479
- References
- 2
- FWCI
- 20.62
- Type
- journal article
- Language
- en
- Open access
- green
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- The paper
- Pillonetto, G., & De Nicolao, G. (2010). A new kernel-based approach for linear system identification. Automatica. https://doi.org/10.1016/j.automatica.2009.10.031
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
