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JMLR
2006
186views more  JMLR 2006»
13 years 8 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
SIAMJO
2010
127views more  SIAMJO 2010»
13 years 2 months ago
Trace Norm Regularization: Reformulations, Algorithms, and Multi-Task Learning
We consider a recently proposed optimization formulation of multi-task learning based on trace norm regularized least squares. While this problem may be formulated as a semidefini...
Ting Kei Pong, Paul Tseng, Shuiwang Ji, Jieping Ye
ICASSP
2011
IEEE
12 years 12 months ago
A Lagrangian dual relaxation approach to ML MIMO detection: Reinterpreting regularized lattice decoding
This paper describes a new approximate maximum-likelihood (ML) MIMO detection approach by studying a Lagrangian dual relaxation (LDR) of ML. Unlike many existing relaxed ML method...
Jiaxian Pan, Wing-Kin Ma
CP
2006
Springer
13 years 12 months ago
The Theory of Grammar Constraints
Abstract. By introducing the Regular Membership Constraint, Gilles Pesant pioneered the idea of basing constraints on formal languages. The paper presented here is highly motivated...
Meinolf Sellmann
SCALESPACE
2007
Springer
14 years 2 months ago
Super-Resolution Using Sub-band Constrained Total Variation
Abstract. Super-resolution of a single image is a severely ill-posed problem in computer vision. It is possible to consider solving this problem by considering a total variation ba...
Priyam Chatterjee, Vinay P. Namboodiri, Subhasis C...