Name
Abstract | ||
|---|---|---|
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 off-the-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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-88138-4_5 | Learning Classifier Systems |
Keywords | DocType | Citations |
learning classifier system,probabilistic model | Conference | 1 |
PageRank | References | Authors |
0.35 | 15 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jan Drugowitsch | 1 | 39 | 3.80 |
| Alwyn M. Barry | 2 | 30 | 2.20 |