SVM-boosting based on Markov resampling: theory and algorithm
type: Item
instance of: scholarly article
title: SVM-boosting based on Markov resampling: theory and algorithm
zbMATH Open document ID: 1480.62024
author: Hongwei Jiang
author: Bin Zou
author: Chen Xu
author: Jie Xu
author: Yuan Yan Tang
published in: Neural Networks
publication date: 2021-12-07T00:00:00Z
Mathematics Subject Classification ID: 62G09
Mathematics Subject Classification ID: 60J20
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 7439673
zbMATH Keywords: boosting
zbMATH Keywords: consistency
zbMATH Keywords: uniformly ergodic Markov chain
zbMATH Keywords: resampling
describes a project that uses: bootstrap
describes a project that uses: XGBoost
describes a project that uses: AdaBoost.MH
MaRDI profile type: Publication
full work available at URL: j.neunet.2020.07.036
OpenAlex ID: W3048868447
Wikidata QID: Q98658431
cites work: The Generalization Ability of Online Algorithms for Dependent Data
cites work: Theory of Reproducing Kernels
cites work: Convexity, Classification, and Risk Bounds
cites work: Q3174158
cites work: Bagging predictors
cites work: Arcing classifiers. (With discussion)
cites work: Q3327527
cites work: Best choices for regularization parameters in learning theory: on the bias-variance problem.
cites work: Q4318617
cites work: Regularization networks and support vector machines
cites work: Boosting a weak learning algorithm by majority
cites work: A decision-theoretic generalization of on-line learning and an application to boosting
cites work: Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
cites work: Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
cites work: On weak base hypotheses and their implications for boosting regression and classification
cites work: Process consistency for AdaBoost.
cites work: Q3785827
cites work: Q4633060
cites work: On the Bayes-risk consistency of regularized boosting methods.
cites work: Q5405255
cites work: Q5214229
cites work: Improved boosting algorithms using confidence-rated predictions
cites work: Weak convergence and empirical processes. With applications to statistics
cites work: Q4261789
cites work: Learning and generalisation. With applications to neural networks.
cites work: Learning rates of least-square regularized regression
cites work: Statistical behavior and consistency of classification methods based on convex risk minimization.
cites work: Boosting with early stopping: convergence and consistency
DOI: 10.1016/J.NEUNET.2020.07.036
description: scientific article; zbMATH DE number 7439673