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Graph-based semi-supervised learning is the problem of propagating labels from a small number of labelled data points to a larger set of unlabelled data.
Logarithmic concave measures and related topics
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Consistency of support vector machines and other regularized kernel classifiers
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M. Belkin, P. Niyogi, and V. Sindhwani · 2006
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Consistency of spectral clustering in stochastic block models
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Uncertainty quantification in graph-based classification of high-dimensional data
A. L. Bertozzi, X. Luo, A. M. Stuart, and K. C. Zygalakis · 2018
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Robust MCMC sampling with non-gaussian and hierarchical priors in high dimensions
V. Chen, M. M. Dunlop, O. Papaspiliopoulos, and A. M. Stuart · 2018
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Spectral partitioning works: Planar graphs and finite element meshes
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On the consistency of multiclass classification methods
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A tutorial on spectral clustering
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Transductive classification via local learning regularization
M. Wu and B. Schölkopf · 2007
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Semi-supervised regression: A recent review
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Bayesian consistency of semi-supervised learning algorithms on graphs: Harmonic function methods
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