Dropout training for SVMs with data augmentation

DOI10.1007/s11704-018-7314-7zbMath1405.68280arXiv1508.02268OpenAlexW1922659888MaRDI QIDQ1713848

Jianfei Chen, Ning Chen, Jun Zhu, Ting Chen

Publication date: 30 January 2019

Published in: Frontiers of Computer Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1508.02268

zbMATH Keywords

logistic regressiondata augmentationdropoutiteratively reweighted least squaresvms

Mathematics Subject Classification ID

Generalized linear models (logistic models) (62J12) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• L-BFGS
• BayesLogit
• ElemStatLearn
• CIFAR

Cites Work

• The dropout learning algorithm
• On the limited memory BFGS method for large scale optimization
• Learning to classify with missing and corrupted features
• Data augmentation for support vector machines
• On the Inductive Bias of Dropout
• Are Loss Functions All the Same?
• Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
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