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ROCAI
2004
Springer
15 years 10 months ago
Learning Mixtures of Localized Rules by Maximizing the Area Under the ROC Curve
We introduce a model class for statistical learning which is based on mixtures of propositional rules. In our mixture model, the weight of a rule is not uniform over the entire ins...
Tobias Sing, Niko Beerenwinkel, Thomas Lengauer
ECML
2007
Springer
15 years 11 months ago
Bayesian Inference for Sparse Generalized Linear Models
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The paramete...
Matthias Seeger, Sebastian Gerwinn, Matthias Bethg...
TMC
2008
123views more  TMC 2008»
15 years 4 months ago
Learning Adaptive Temporal Radio Maps for Signal-Strength-Based Location Estimation
In wireless networks, a client's locations can be estimated using signal strength received from signal transmitters. Static fingerprint-based techniques are commonly used for ...
Jie Yin, Qiang Yang, Lionel M. Ni
CORR
2012
Springer
170views Education» more  CORR 2012»
14 years 22 days 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
IJCAI
2007
15 years 6 months ago
Learning Semantic Descriptions of Web Information Sources
The Internet is full of information sources providing various types of data from weather forecasts to travel deals. These sources can be accessed via web-forms, Web Services or RS...
Mark James Carman, Craig A. Knoblock