Abstract
We review the Distributional Clauses Particle Filter (DCPF), a statistical relational framework for inference in hybrid domains over time such as vision and robotics. Applications in these domains are challenging for statistical relational learning as they require dealing with continuous distributions and dynamics in real-time. The framework addresses these issues, it supports the online learning of parameters and it was tested in several tracking scenarios with good results. © 2014 Springer-Verlag.
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CITATION STYLE
Nitti, D., De Laet, T., & De Raedt, L. (2014). Distributional clauses particle filter. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8726 LNAI, pp. 504–507). Springer Verlag. https://doi.org/10.1007/978-3-662-44845-8_45
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