Kernel methods in system identification, machine learning and function estimation: A survey

Gianluigi Pillonetto; Francesco Dinuzzo; Tianshi Chen; Giuseppe De Nicolao; Lennart Ljung

doi:10.1016/j.automatica.2014.01.001

Kernel methods in system identification, machine learning and function estimation: A survey

Automatica ◽

10.1016/j.automatica.2014.01.001 ◽

2014 ◽

Vol 50 (3) ◽

pp. 657-682 ◽

Cited By ~ 294

Author(s):

Gianluigi Pillonetto ◽

Francesco Dinuzzo ◽

Tianshi Chen ◽

Giuseppe De Nicolao ◽

Lennart Ljung

Keyword(s):

Machine Learning ◽

System Identification ◽

Kernel Methods ◽

Function Estimation ◽

And Function

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On Semiseparable Kernels and Efficient Computation of Regularized System Identification and Function Estimation

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.222 ◽

2020 ◽

Vol 53 (2) ◽

pp. 462-467

Author(s):

Tianshi Chen ◽

Martin S. Andersen

Keyword(s):

System Identification ◽

Function Estimation ◽

Efficient Computation ◽

And Function

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On semiseparable kernels and efficient implementation for regularized system identification and function estimation

Automatica ◽

10.1016/j.automatica.2021.109682 ◽

2021 ◽

Vol 132 ◽

pp. 109682

Author(s):

Tianshi Chen ◽

Martin S. Andersen

Keyword(s):

System Identification ◽

Efficient Implementation ◽

Function Estimation ◽

And Function

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On kernel structures for regularized system identification (I): a machine learning perspective**This work has been supported by a research grant for junior researchers No. 621-2014-5894 and the Linnaeus Center CADICS, both funded by the Swedish Research Council, and the ERC advanced grant LEARN, No. 267381, funded by the European Research Council.http://www.hamecmopsys.ens2m.fr.

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.267 ◽

2015 ◽

Vol 48 (28) ◽

pp. 1035-1040 ◽

Cited By ~ 5

Author(s):

Tianshi Chen ◽

Lennart Ljung

Keyword(s):

Machine Learning ◽

System Identification ◽

Research Council ◽

European Research ◽

Swedish Research Council ◽

Research Grant

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Nonlinear System Identification of a Furuta Pendulum Using Machine Learning Techniques

PAMM ◽

10.1002/pamm.202000036 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Tobias Rückwald ◽

Svenja Drücker ◽

Daniel-André Dücker ◽

Robert Seifried

Keyword(s):

Machine Learning ◽

System Identification ◽

Nonlinear System ◽

Nonlinear System Identification ◽

Machine Learning Techniques ◽

Learning Techniques

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A Generic Sensor Fusion Problem: Classification and Function Estimation

Multiple Classifier Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-540-25966-4_2 ◽

2004 ◽

pp. 16-30 ◽

Cited By ~ 6

Author(s):

Nageswara S. V. Rao

Keyword(s):

Sensor Fusion ◽

Function Estimation ◽

And Function

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Near-Linear Time Local Polynomial Nonparametric Estimation with Box Kernels

INFORMS Journal on Computing ◽

10.1287/ijoc.2020.1021 ◽

2021 ◽

Author(s):

Yining Wang ◽

Yi Wu ◽

Simon S. Du

Keyword(s):

Operations Management ◽

Polynomial Regression ◽

Linear Time ◽

Big Data Analytics ◽

Function Estimation ◽

Local Polynomial ◽

Efficiency And Effectiveness ◽

Exploratory Data ◽

And Function ◽

Novel Applications

Summary of Contribution: Big data analytics has become essential for modern operations research and operations management applications. Statistics methods, such as nonparametric density and function estimation, play important roles in predictive and exploratory data analysis for economics and operations management problems. In this paper, we concentrate on efficiently computing local polynomial regression estimates. We significantly accelerate the computation of such local polynomial estimates by novel applications of multidimensional binary indexed trees ( Fenwick 1994 ) and lazy memory allocation via hashing. Both time and space complexity of our proposed algorithm are nearly linear in the number of inputs. Simulation results confirm the efficiency and effectiveness of our proposed methods.

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