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» Principles of Lifelong Learning for Predictive User Modeling
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NIPS
2001
13 years 6 months ago
Probabilistic principles in unsupervised learning of visual structure: human data and a model
To find out how the representations of structured visual objects depend on the co-occurrence statistics of their constituents, we exposed subjects to a set of composite images wit...
Shimon Edelman, Benjamin P. Hiles, Hwajin Yang, Na...
ICML
2010
IEEE
13 years 6 months ago
Modeling Interaction via the Principle of Maximum Causal Entropy
The principle of maximum entropy provides a powerful framework for statistical models of joint, conditional, and marginal distributions. However, there are many important distribu...
Brian Ziebart, J. Andrew Bagnell, Anind K. Dey
IJCAI
2001
13 years 6 months ago
Leveraging Data About Users in General in the Learning of Individual User Models
Models of computer users that are learned on the basis of data can make use of two types of information: data about users in general and data about the current individual user. Fo...
Anthony Jameson, Frank Wittig
ICPR
2006
IEEE
14 years 6 months ago
A New Data Selection Principle for Semi-Supervised Incremental Learning
Current semi-supervised incremental learning approaches select unlabeled examples with predicted high confidence for model re-training. We show that for many applications this dat...
Alexander I. Rudnicky, Rong Zhang
CVPR
2011
IEEE
13 years 12 days ago
Learning structured prediction models for interactive image labeling
We propose structured models for image labeling that take into account the dependencies among the image labels explicitly. These models are more expressive than independent label ...
Thomas Mensink, Jakob Verbeek, Gabriela Csurka