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. © 2009 Springer Berlin Heidelberg.
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CITATION STYLE
Aznar, F., Pujol, F. A., Pujol, M., & Rizo, R. (2009). Using Gaussian processes in Bayesian robot programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5518 LNCS, pp. 547–553). https://doi.org/10.1007/978-3-642-02481-8_79
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