Mixtures of Gaussians are among the most fundamental and widely used statistical models. Current techniques for learning such mixtures from data are local search heuristics with w...
XCS with Computed Action, briefly XCSCA, is a recent extension of XCS to tackle problems involving a large number of discrete actions. In XCSCA the classifier action is computed wi...
Potential-based shaping was designed as a way of introducing background knowledge into model-free reinforcement-learning algorithms. By identifying states that are likely to have ...
In recent years, a number of machine learning algorithms have been developed for the problem of ordinal classification. These algorithms try to exploit, in one way or the other, t...
We present an algorithm, called the offset tree, for learning in situations where a loss associated with different decisions is not known, but was randomly probed. The algorithm i...