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ACL
2009
13 years 1 months ago
Distributional Representations for Handling Sparsity in Supervised Sequence-Labeling
Supervised sequence-labeling systems in natural language processing often suffer from data sparsity because they use word types as features in their prediction tasks. Consequently...
Fei Huang, Alexander Yates
ACL
2010
13 years 1 months ago
Practical Very Large Scale CRFs
Conditional Random Fields (CRFs) are a widely-used approach for supervised sequence labelling, notably due to their ability to handle large description spaces and to integrate str...
Thomas Lavergne, Olivier Cappé, Franç...
JMLR
2006
186views more  JMLR 2006»
13 years 3 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
JSAC
2011
185views more  JSAC 2011»
12 years 10 months ago
Joint Dynamic Resource Allocation and Waveform Adaptation for Cognitive Networks
This paper investigates the issue of dynamic resource allocation (DRA) in the context of multiuser cognitive radio networks. We present a general framework adopting generalized si...
Zhi Tian, Geert Leus, Vincenzo Lottici
CVPR
2010
IEEE
13 years 7 months ago
Classification and Clustering via Dictionary Learning with Structured Incoherence
A clustering framework within the sparse modeling and dictionary learning setting is introduced in this work. Instead of searching for the set of centroid that best fit the data, ...
Pablo Sprechmann, Ignacio Ramirez, Guillermo Sapir...