relation: http://eprints.imtlucca.it/3128/
title: Online learning as an LQG optimal control problem with random matrices
creator: Gnecco, Giorgio
creator: Bemporad, Alberto
creator: Gori, Marco
creator: Morisi, Rita
creator: Sanguineti, Marcello
subject: QA75 Electronic computers. Computer science
description: In this paper, we combine optimal control theory and machine learning techniques to propose and solve an optimal control formulation of online learning from supervised examples, which are used to learn an unknown vector parameter modeling the relationship between the input examples and their outputs. We show some connections of the problem investigated with the classical LQG optimal control problem, of which the proposed problem is a non-trivial variation, as it involves random matrices. We also compare the optimal solution to the proposed problem with the Kalman-filter estimate of the parameter vector to be learned, demonstrating its larger smoothness and robustness to outliers. Extension of the proposed online-learning framework are mentioned at the end of the paper.
publisher: IEEE
date: 2015-07
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Gnecco, Giorgio and Bemporad, Alberto and Gori, Marco and Morisi, Rita and Sanguineti, Marcello  Online learning as an LQG optimal control problem with random matrices.  In: 14th European Control Conference (ECC), July 15-17, 2015, Linz, Austria pp. 2482-2489.  ISBN 978-3-9524269-3-7.      (2015)  
relation: http://dx.doi.org/10.1109/ECC.2015.7330911
relation: 10.1109/ECC.2015.7330911