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...
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...
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 ...
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...
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...