ABSTRACT A Principled Foundation for LCS

In this paper we explicitly identify the probabilistic model underlying LCS by linking it to a generalisation of the common Mixture-of-Experts model. Having an explicit representation of the model not only puts LCS on a strong statistical foundation and identifies the assumptions that the model makes about the data, but also allows us to use offthe-shelf training methods to train it. We show how to exploit this advantage by embedding the LCS model into a fully Bayesian framework that results in an objective function for a set of classifiers, effectively turning the LCS training into a principled optimisation task. A set of preliminary experiments demonstrate the feasibility of this approach