Dropout training for SVMs with data augmentation
type: Item
instance of: scholarly article
title: Dropout training for SVMs with data augmentation
zbMATH Open document ID: 1405.68280
author: Ning Chen
author: J. Zhu
author: Jianfei Chen
author: Ting Chen
published in: Frontiers of Computer Science
publication date: 2019-01-30T00:00:00Z
full work available at URL: https://arxiv.org/abs/1508.02268
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62J12
zbMATH DE Number: 7008374
zbMATH Keywords: dropout
zbMATH Keywords: svms
zbMATH Keywords: logistic regression
zbMATH Keywords: data augmentation
zbMATH Keywords: iteratively reweighted least square
describes a project that uses: L-BFGS
describes a project that uses: ElemStatLearn
describes a project that uses: BayesLogit
describes a project that uses: CIFAR
MaRDI profile type: Publication
OpenAlex ID: W1922659888
arXiv ID: 1508.02268
cites work: Q2934059
cites work: Q4856771
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cites work: Are Loss Functions All the Same?
cites work: The dropout learning algorithm
cites work: On the inductive bias of dropout
cites work: Q4386941
cites work: Q2934023
cites work: Q3723577
cites work: On the limited memory BFGS method for large scale optimization
cites work: Q3093184
DOI: 10.1007/S11704-018-7314-7
description: scientific article