The kernel Kalman rule. Efficient nonparametric inference by recursive least-squares and subspace projections
type: Item
instance of: scholarly article
title: The kernel Kalman rule. Efficient nonparametric inference by recursive least-squares and subspace projections
zbMATH Open document ID: 1447.62033
author: Gregor H. W. Gebhardt
author: Andras Kupcsik
author: Gerhard Neumann
published in: Machine Learning
publication date: 2019-11-26T00:00:00Z
Mathematics Subject Classification ID: 62G05
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 7136682
zbMATH Keywords: nonparametric inference
zbMATH Keywords: kernel methods
zbMATH Keywords: state estimation
zbMATH Keywords: model learning
describes a project that uses: CMA-ES
MaRDI profile type: Publication
full work available at URL: s10994-019-05816-z
OpenAlex ID: W2950429510
cites work: Q3150769
cites work: Theory of Reproducing Kernels
cites work: Sparse on-line Gaussian processes
cites work: Q3093333
cites work: Towards a new evolutionary computation. Advances on estimation of distribution algorithms.
cites work: A spectral algorithm for learning hidden Markov models
cites work: Q3148817
cites work: A Hilbert Space Embedding for Distributions
cites work: Learning Nonlinear Generative Models of Time Series With a Kalman Filter in RKHS
Wikidata QID: Q127672187
DOI: 10.1007/S10994-019-05816-Z
description: scientific article; zbMATH DE number 7136682