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. Categories and Subject Descriptors G.3 [Probability and Statistics]: Probabilistic Algorithms;

• Type: Conference
• Year: 2007
• Where: IWCLS
• Authors: Jan Drugowitsch, Alwyn Barry
• Keywords: Common Mixture-of-experts Model, IWCLS 2007, LCS Model, Machine Learning, Model Underlying Lcs