Name
Abstract | ||
|---|---|---|
In this paper, we present an adaptation of Gaussian Processes for learning a joint probabilistic distribution using Bayesian Programming. More specifically, a robot navigation problem will be showed as a case of study. In addition, Gaussian Processes will be compared with one of the most popular techniques for machine learning: Neural Networks. Finally, we will discuss about the accuracy of these methods and will conclude proposing some future lines for this research. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02481-8_79 | IWANN (2) |
Keywords | Field | DocType |
gaussian process | Robot learning,Computer science,Inductive programming,Bayesian programming,Gaussian process,Artificial intelligence,Artificial neural network,Robot,Machine learning,Robot programming,Bayesian probability | Conference |
Volume | ISSN | Citations |
5518 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fidel Aznar Gregori | 1 | 17 | 5.31 |
| Francisco A. Pujol | 2 | 33 | 8.87 |
| Mar Pujol López | 3 | 28 | 8.54 |
| R. Rizo | 4 | 51 | 14.90 |