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NIPS
2004
15 years 8 months ago
Semi-supervised Learning by Entropy Minimization
We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regulariza...
Yves Grandvalet, Yoshua Bengio
NAACL
2010
15 years 4 months ago
Not All Seeds Are Equal: Measuring the Quality of Text Mining Seeds
Open-class semantic lexicon induction is of great interest for current knowledge harvesting algorithms. We propose a general framework that uses patterns in bootstrapping fashion ...
Zornitsa Kozareva, Eduard H. Hovy
ICML
2005
IEEE
16 years 7 months ago
Learning as search optimization: approximate large margin methods for structured prediction
Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., li...
Daniel Marcu, Hal Daumé III
ISBI
2006
IEEE
16 years 7 months ago
Coronary tree extraction from X-ray angiograms using marked point processes
In this paper, we use marked point processes to perform an unsupervised extraction of the coronary tree from 2D X-ray angiography. These processes provide a rigorous framework bas...
Caroline Lacoste, Gérard Finet, Isabelle E....
ICASSP
2009
IEEE
16 years 1 months ago
Fast bayesian compressive sensing using Laplace priors
In this paper we model the components of the compressive sensing (CS) problem using the Bayesian framework by utilizing a hierarchical form of the Laplace prior to model sparsity ...
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag...