Mikhail (Misha)  Belkin
Mikhail (Misha) Belkin
Professor of Data Science, Halıcıoğlu Data Science Institute, UCSD
Dirección de correo verificada de ucsd.edu - Página principal
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Laplacian eigenmaps for dimensionality reduction and data representation
M Belkin, P Niyogi
Neural computation 15 (6), 1373-1396, 2003
75312003
Laplacian eigenmaps and spectral techniques for embedding and clustering
M Belkin, P Niyogi
Advances in neural information processing systems, 585-591, 2002
44352002
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
M Belkin, P Niyogi, V Sindhwani
Journal of machine learning research 7 (Nov), 2399-2434, 2006
37642006
Semi-supervised learning on Riemannian manifolds
M Belkin, P Niyogi
Machine learning 56 (1-3), 209-239, 2004
8932004
Regularization and semi-supervised learning on large graphs
M Belkin, I Matveeva, P Niyogi
International Conference on Computational Learning Theory, 624-638, 2004
6502004
Towards a theoretical foundation for Laplacian-based manifold methods
M Belkin, P Niyogi
Journal of Computer and System Sciences 74 (8), 1289-1308, 2008
5582008
Consistency of spectral clustering
U Von Luxburg, M Belkin, O Bousquet
The Annals of Statistics, 555-586, 2008
5402008
Beyond the point cloud: from transductive to semi-supervised learning
V Sindhwani, P Niyogi, M Belkin
Proceedings of the 22nd international conference on Machine learning, 824-831, 2005
5212005
A co-regularization approach to semi-supervised learning with multiple views
V Sindhwani, P Niyogi, M Belkin
Proceedings of ICML workshop on learning with multiple views 2005, 74-79, 2005
4132005
On Manifold Regularization.
M Belkin, P Niyogi, V Sindhwani
AISTATS, 2005
3362005
Using Manifold Structure for Partially Labeled Classification
M Belkin, P Niyogi
NIPS 2002, 2003
3352003
Laplacian support vector machines trained in the primal
S Melacci, M Belkin
Journal of Machine Learning Research 12 (31), 1149−1184, 2011
3312011
Convergence of Laplacian eigenmaps
M Belkin, P Niyogi
Advances in Neural Information Processing Systems, 129-136, 2007
2522007
Reconciling modern machine-learning practice and the classical bias–variance trade-off
M Belkin, D Hsu, S Ma, S Mandal
Proceedings of the National Academy of Sciences 116 (32), 15849-15854, 2019, 2019
249*2019
Discrete laplace operator on meshed surfaces
M Belkin, J Sun, Y Wang
Proceedings of the twenty-fourth annual symposium on Computational geometry …, 2008
2432008
Polynomial learning of distribution families
M Belkin, K Sinha
SIAM Journal on Computing 44 (4), 889-911, 2015
1942015
Constructing Laplace Operator from Point Clouds in ℝd
M Belkin, J Sun, Y Wang
Proceedings of the twentieth annual ACM-SIAM symposium on Discrete …, 2009
1802009
Problems of learning on manifolds
M Belkin
University of Chicago, Dept. of Mathematics, 2003
1692003
On learning with integral operators.
L Rosasco, M Belkin, E De Vito
Journal of Machine Learning Research 11 (2), 2010
1592010
To understand deep learning we need to understand kernel learning
M Belkin, S Ma, S Mandal
The 35th International Conference on Machine Learning (ICML 2018), 2018
1372018
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20