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This article provides an original understanding of the behavior of a class of graph-oriented semi-supervised learning algorithms in the limit of large and numerous data.
Probability and Measure
P. Billingsley · 1995
Earlier work this paper cites.
The mnist database of handwritten digits, 1998
Yann LeCun, Corinna Cortes, and Christopher JC Burges · 1998
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Learning with kernels: support vector machines, regularization, optimization, and beyond
Bernhard Schölkopf and Alexander J Smola · 2002
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Abla Kammoun and Romain Couillet · 2017
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A large dimensional analysis of least squares support vector machines
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