Hermite learning with gradient data
type: Item
instance of: scholarly article
title: Hermite learning with gradient data
zbMATH Open document ID: 1191.68532
author: N. E. Zubov
published in: Journal of Computational and Applied Mathematics
publication date: 2010-03-04T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62J02
zbMATH DE Number: 5677349
zbMATH Keywords: learning theory
zbMATH Keywords: Hermite learning
zbMATH Keywords: reproducing kernel Hilbert spaces
zbMATH Keywords: representer theorem
zbMATH Keywords: sampling operator
zbMATH Keywords: integral operator
MaRDI profile type: Publication
full work available at URL: j.cam.2009.11.059
OpenAlex ID: W2058531275
cites work: Derivative reproducing properties for kernel methods in learning theory
cites work: Combined \(\ell_{2}\) data and gradient fitting in conjunction with \(\ell_{1}\) regularization
cites work: Estimating surface normals in noisy point cloud data
cites work: Q3773148
cites work: Q3093358
cites work: Q3174048
cites work: Theory of Reproducing Kernels
cites work: Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
cites work: Geometry on probability spaces
cites work: Learning Theory
cites work: Model selection for regularized least-squares algorithm in learning theory
cites work: Leave-One-Out Bounds for Kernel Methods
cites work: Shannon sampling. II: Connections to learning theory
cites work: On early stopping in gradient descent learning
cites work: Capacity of reproducing kernel spaces in learning theory
cites work: Learning theory estimates via integral operators and their approximations
cites work: Shannon sampling and function reconstruction from point values
cites work: Q3093228
cites work: The covering number in learning theory
DOI: 10.1016/J.CAM.2009.11.059
description: scientific article; zbMATH DE number 5677349