Regularized System Identification Learning Dynamic Models from Data

This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learn...

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Bibliografski detalji
Glavni autor:	Pillonetto, Gianluigi (auth)
Daljnji autori:	Chen, Tianshi (auth), Chiuso, Alessandro (auth), De Nicolao, Giuseppe (auth), Ljung, Lennart (auth)
Format:	Elektronički Poglavlje knjige
Jezik:	engleski
Izdano:	Cham Springer Nature 2022
Serija:	Communications and Control Engineering
Teme:	Machine learning Automatic control engineering Statistical physics Bayesian inference Probability & statistics Cybernetics & systems theory System Identification Machine Learning Linear Dynamical Systems Nonlinear Dynamical Systems Kernel-based Regularization Bayesian Interpretation of Regularization Gaussian Processes Reproducing Kernel Hilbert Spaces Estimation Theory Support Vector Machines Regularization Networks
Online pristup:	OAPEN Library: download the publication OAPEN Library: description of the publication
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Regularized System Identification Learning Dynamic Models from Data / od Pillonetto, Gianluigi, Chen, Tianshi, Chiuso, Alessandro, De Nicolao, Giuseppe, Ljung, Lennart

Izdano 2022

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Elektronički e-knjiga

Regularized System Identification Learning Dynamic Models from Data od Pillonetto, Gianluigi

Izdano 2022

DOAB: download the publication
DOAB: description of the publication

Elektronički Poglavlje knjige

Regularized System Identification Learning Dynamic Models from Data

Regularized System Identification Learning Dynamic Models from Data / od Pillonetto, Gianluigi, Chen, Tianshi, Chiuso, Alessandro, De Nicolao, Giuseppe, Ljung, Lennart

Regularized System Identification Learning Dynamic Models from Data od Pillonetto, Gianluigi

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