Support vector machine with manifold regularization and partially labeling privacy protection

DOI10.1016/J.INS.2014.09.050zbMath1360.68702OpenAlexW2028704235MaRDI QIDQ526695

Tongguang Ni, Shi-Tong Wang, Fu-lai Chung

Publication date: 15 May 2017

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2014.09.050

zbMATH Keywords

classificationprivacy protectionsupport vector machinelarge datasetsmanifold regularization

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

HesGCN: Hessian graph convolutional networks for semi-supervised classification ⋮ \(L_{1}\)-norm loss based twin support vector machine for data recognition ⋮ Semi-supervised active learning for support vector machines: a novel approach that exploits structure information in data ⋮ Laplacian unit-hyperplane learning from positive and unlabeled examples

Uses Software

• LIBSVM

Cites Work

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• Graph-based semi-supervised learning by mixed label propagation with a soft constraint
• A statistical perspective on ill-posed inverse problems (with discussion)
• Nonparallel hyperplane support vector machine for binary classification problems
• A Parallel Mixture of SVMs for Very Large Scale Problems
• Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
• The elements of statistical learning. Data mining, inference, and prediction
• Training invariant support vector machines

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