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ICML
2005
IEEE
14 years 11 months ago
Learning first-order probabilistic models with combining rules
Many real-world domains exhibit rich relational structure and stochasticity and motivate the development of models that combine predicate logic with probabilities. These models de...
Sriraam Natarajan, Prasad Tadepalli, Eric Altendor...
ICDM
2009
IEEE
233views Data Mining» more  ICDM 2009»
14 years 5 months ago
Semi-Supervised Sequence Labeling with Self-Learned Features
—Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of l...
Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko ...
UM
2005
Springer
14 years 3 months ago
Modeling Individual and Collaborative Problem Solving in Medical Problem-Based Learning
Abstract. Since problem solving in group problem-based learning is a collaborative process, modeling individuals and the group is necessary if we wish to develop an intelligent tut...
Siriwan Suebnukarn, Peter Haddawy
ICMI
2010
Springer
141views Biometrics» more  ICMI 2010»
13 years 8 months ago
Learning and evaluating response prediction models using parallel listener consensus
Traditionally listener response prediction models are learned from pre-recorded dyadic interactions. Because of individual differences in behavior, these recordings do not capture...
Iwan de Kok, Derya Ozkan, Dirk Heylen, Louis-Phili...
CORR
2012
Springer
170views Education» more  CORR 2012»
12 years 6 months ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson