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.
This work has been supported by the Conselleria d’Educació of the Generalitat Valenciana, project GVPRE/2008/040, and by the Universidad de Alicante, project GRE08P02.
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Aznar, F., Pujol, F.A., Pujol, M., Rizo, R. (2009). Using Gaussian Processes in Bayesian Robot Programming. In: Omatu, S., et al. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol 5518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02481-8_79
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DOI: https://doi.org/10.1007/978-3-642-02481-8_79
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