Ed Snelson won the Predictive Uncertainty in Environmental Modelling Competition in the temp(erature) category using this algorithm. Some characteristics of the algorithm are:

- Gradient descent
- … on about 600 parameters
- … with local minima
- … to solve regression.

This bears a strong resemblance to a neural network. The two main differences seem to be:

- The system has a probabilistic interpretation (which may aid design).
- There are (perhaps) fewer parameters than a typical neural network might have for the same problem (aiding speed).

If any undergraduates, grad students, or postdocs want to get involved in another machine learning contest, see The Fair Isaac Student Data Mining Competition. The competition is on document classification and modeling.