On the convergence rate of kernel-based sequential greedy regression
type: Item
instance of: scholarly article
title: On the convergence rate of kernel-based sequential greedy regression
zbMATH Open document ID: 1256.68137
DOI: 10.1155/2012/619138
author: Xiaoyin Wang
author: Xiaoyan Wei
author: Zhibin Pan
published in: Abstract and Applied Analysis
publication date: 2013-02-04T00:00:00Z
review text: Summary: A kernel-based greedy algorithm is presented to realize efficient sparse learning with data-dependent basis functions. Upper bound of generalization error is obtained based on the complexity measure of hypothesis space with covering numbers. A careful analysis shows the error has a satisfactory decay rate under mild conditions.
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62J02
zbMATH DE Number: 6134129
Wikidata QID: Q58695704
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/619138
OpenAlex ID: W2061751626
cites work: On the mathematical foundations of learning
cites work: Learning Theory
cites work: Learning with sample dependent hypothesis spaces
cites work: Learning by nonsymmetric kernels with data dependent spaces and \(\ell^1\)-regularizer
cites work: Concentration estimates for learning with \(\ell ^{1}\)-regularizer and data dependent hypothesis spaces
cites work: Unified approach to coefficient-based regularized regression
cites work: Error bounds for \(l^p\)-norm multiple kernel learning with least square loss
cites work: Consistency analysis of spectral regularization algorithms
cites work: Approximation and learning by greedy algorithms
cites work: 10.1162/153244304773936108
cites work: Sequential greedy approximation for certain convex optimization problems
cites work: Learning rates of multi-kernel regression by orthogonal greedy algorithm
cites work: Approximation Bounds for Some Sparse Kernel Regression Algorithms
cites work: Q3093220
cites work: Error bounds of multi-graph regularized semi-supervised classification
cites work: The convergence rate of a regularized ranking algorithm
cites work: Concentration estimates for learning with unbounded sampling
cites work: Multi-kernel regularized classifiers
description: scientific article; zbMATH DE number 6134129