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» Approximation Methods for Supervised Learning
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JMLR
2006
186views more  JMLR 2006»
15 years 4 months ago
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
Mikhail Belkin, Partha Niyogi, Vikas Sindhwani

Publication
218views
13 years 9 months ago
Comparing Visual Feature Coding for Learning Disjoint Camera Dependencies
This paper systematically investigates the effectiveness of different visual feature coding schemes for facilitating the learning of time-delayed dependencies among disjoint multi-...
Xiatian Zhu, Shaogang Gong, and Chen Change Loy
ICML
2009
IEEE
15 years 11 months ago
Learning linear dynamical systems without sequence information
Virtually all methods of learning dynamic systems from data start from the same basic assumption: that the learning algorithm will be provided with a sequence, or trajectory, of d...
Tzu-Kuo Huang, Jeff Schneider
ICDM
2009
IEEE
112views Data Mining» more  ICDM 2009»
15 years 11 months ago
Resolving Identity Uncertainty with Learned Random Walks
A pervasive problem in large relational databases is identity uncertainty which occurs when multiple entries in a database refer to the same underlying entity in the world. Relati...
Ted Sandler, Lyle H. Ungar, Koby Crammer
JMLR
2010
172views more  JMLR 2010»
14 years 11 months ago
Modeling annotator expertise: Learning when everybody knows a bit of something
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this p...
Yan Yan, Rómer Rosales, Glenn Fung, Mark W....