Kernel methods in system identification, machine learning and function estimation: A survey
By Gianluigi Pillonetto, Francesco Dinuzzo, Tianshi Chen, Giuseppe De Nicolao, Lennart Ljung. at - Automatisierungstechnik, 2014
776 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/1b310cd2322cb14faa7d99038717c313a9441539
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Steven L. Brunton
- J. Nathan Kutz
- Eurika Kaiser
- Lukas Hewing
- Melanie Nicole Zeilinger
- Juraj Kabzan
- Joshua L. Proctor
- Maizar Raissi
In Control and Systems Engineering
- Steven L. Brunton
- J. Nathan Kutz
- Eurika Kaiser
- Lukas Hewing
- Melanie Nicole Zeilinger
- Juraj Kabzan
- Joshua L. Proctor
- Maizar Raissi
Citations in the corpus
- Cautious Model Predictive Control Using Gaussian Process Regression (2017, methodology): “In particular nonparametric methods, which have seen wide success in machine learning, offer significant potential for control [5].”
- Sparse identification of nonlinear dynamics for model predictive control in the low-data limit (2017, methodology): “We refer the reader to an extensive review on nonlinear system identification methods [39] and a recent review in the context of machine learning [40].”
- Learning to Predict Without Looking Ahead: World Models Without Forward Prediction (2019, neutral): “learnable parameters for system identification [45].”