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» Learning from General Label Constraints
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TIP
2010
182views more  TIP 2010»
13 years 3 months ago
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the ...
Feiping Nie, Dong Xu, Ivor Wai-Hung Tsang, Changsh...
AAAI
1994
13 years 10 months ago
GENET: A Connectionist Architecture for Solving Constraint Satisfaction Problems by Iterative Improvement
New approaches to solving constraint satisfaction problems using iterative improvement techniques have been found to be successful on certain, very large problems such as the mill...
Andrew J. Davenport, Edward P. K. Tsang, Chang J. ...
CORR
1999
Springer
118views Education» more  CORR 1999»
13 years 8 months ago
Supervised Grammar Induction Using Training Data with Limited Constituent Information
Corpus-based grammar induction generally relies on hand-parsed training data to learn the structure of the language. Unfortunately, the cost of building large annotated corpora is...
Rebecca Hwa
CVPR
2010
IEEE
14 years 4 months ago
Efficient Piecewise Learning for Conditional Random Fields
Conditional Random Field models have proved effective for several low-level computer vision problems. Inference in these models involves solving a combinatorial optimization probl...
Karteek Alahari, Phil Torr
ISCI
2007
170views more  ISCI 2007»
13 years 8 months ago
Automatic learning of cost functions for graph edit distance
Graph matching and graph edit distance have become important tools in structural pattern recognition. The graph edit distance concept allows us to measure the structural similarit...
Michel Neuhaus, Horst Bunke