Name
Abstract | ||
|---|---|---|
In this paper a method for learning and representing joint probabilistic distributions, using binary trees, is shown. This method could be used with the Bayesian Programming formalism, being a very useful tool when working with real world data. It has the advantage of learning unknown probabilistic distributions directly from raw data, and to remain more balanced than other previous methods. Finally, an application to learn a fuzzy control system, using this approach, will be presented. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11875581_57 | IDEAL |
Keywords | Field | DocType |
discrete probability distribution,fuzzy control system,bayesian programming formalism,joint probabilistic distribution,binary tree,real world data,previous method,useful tool,multi-resolution binary tree,raw data,unknown probabilistic distribution,fuzzy control,probability distribution | Joint probability distribution,Computer science,Multiresolution analysis,Binary tree,Probability distribution,Bayesian programming,Artificial intelligence,Probabilistic logic,Fuzzy control system,Random binary tree,Machine learning | Conference |
Volume | ISSN | ISBN |
4224 | 0302-9743 | 3-540-45485-3 |
Citations | PageRank | References |
1 | 0.36 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| F. A. Sanchís | 1 | 1 | 0.36 |
| F. Aznar | 2 | 1 | 0.36 |
| Mireia Sempere | 3 | 4 | 2.48 |
| Mar Pujol López | 4 | 28 | 8.54 |
| R. Rizo | 5 | 51 | 14.90 |