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157
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JMLR
2006
186views more  JMLR 2006»
15 years 2 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
119
Voted
CORR
2002
Springer
99views Education» more  CORR 2002»
15 years 2 months ago
The Identification of Context-Sensitive Features: A Formal Definition of Context for Concept Learning
A large body of research in machine learning is concerned with supervised learning from examples. The examples are typically represented as vectors in a multi-dimensional feature ...
Peter D. Turney
187
Voted

Publication
218views
13 years 7 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
100
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ICCV
2009
IEEE
16 years 7 months ago
Efficient Discriminative Learning of Parts-based Models
Supervised learning of a parts-based model can be for- mulated as an optimization problem with a large (exponen- tial in the number of parts) set of constraints. We show how thi...
M. Pawan Kumar, Andrew Zisserman, Philip H.S. Torr
113
Voted
CVPR
2001
IEEE
16 years 4 months ago
Bayesian Learning of Sparse Classifiers
Bayesian approaches to supervised learning use priors on the classifier parameters. However, few priors aim at achieving "sparse" classifiers, where irrelevant/redundant...
Anil K. Jain, Mário A. T. Figueiredo