M. Belkin and P. Niyogi. Semi-supervised learning on Riemannian manifolds. Mach. Learn., 56: 209--239, 2004. Home/Search Document Not in Database Summary Related Articles Check |
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Semi-Supervised Learning with Trees - Kemp, Griffiths, Stromsten.. (2003) (Correct)
....margin of C on the unlabeled data, effectively assume that C is maximally smooth with respect to the density of unlabeled data in feature space. The Laplacian method of Belkin and Niyogi assumes the data concentrate on a low dimensional (Riemannian) manifold, and that C is smooth on that manifold [2]. The manifold geometry is estimated from the unlabeled data using simple bottom up techniques based on a graph of nearest neighbors. This approach performs quite well in classifying data with a natural manifold structure, e.g. handwritten digits. Methods of [3] and [4] combine aspects of both ....
....features of objects, X u , given ) We can estimate through more or less sophisticated means. The experiments in Section 3 use a greedy bottomup method (average link agglomerative clustering on the full data X) that is comparable in complexity to the neighborhood graph constructions used in [2]. 3 Experiments: tree based versus manifold based approaches We compared four algorithms: TBB, TNN, the Laplacian approach of Belkin and Niyogi, and generic Nearest Neighbor. We used four data sets which we expected to have a tree like structure, because they arose from biological taxonomies ....
M. Belkin and P. Niyogi. Semi-supervised learning on Riemannian manifolds. 2002. Submitted to Journal of Machine Learning Research.
Combining Graph Laplacians for Semi-Supervised Learning - Argyriou, Herbster, Pontil (2005) (Correct)
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M. Belkin and P. Niyogi. Semi-supervised learning on Riemannian manifolds. Mach. Learn., 56: 209--239, 2004.
Journal of Machine Learning Research 7 (2006) 519--549.. - Sayan Mukherjee Sayan (Correct)
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M. Belkin and P. Niyogi. Semi-Supervised Learning on Riemannian Manifolds. Machine Learning, 56(1-3):209--239, 2004.
Boosting on Manifolds: Adaptive Regularization of Base Classifiers - Kegl, Wang (Correct)
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M. Belkin and P. Niyogi, "Semi-supervised learning on Riemannian manifolds," Machine Learning, to appear, 2004.
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