Distributional Clauses Particle Filter

Nitti, Davide; De Laet, Tinne; De Raedt, Luc

doi:10.1007/978-3-662-44845-8_45

Distributional Clauses Particle Filter

Davide Nitti, Tinne De Laet, Luc De Raedt

ECML-PKDD 2014 pp. 504-507

doi:10.1007/978-3-662-44845-8_45 /ecmlpkdd/2014/nitti2014ecmlpkdd-distributional/

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.

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Cite

Text

Nitti et al. "Distributional Clauses Particle Filter." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014. doi:10.1007/978-3-662-44845-8_45

Markdown

[Nitti et al. "Distributional Clauses Particle Filter." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014.](https://mlanthology.org/ecmlpkdd/2014/nitti2014ecmlpkdd-distributional/) doi:10.1007/978-3-662-44845-8_45

BibTeX

@inproceedings{nitti2014ecmlpkdd-distributional,
  title     = {{Distributional Clauses Particle Filter}},
  author    = {Nitti, Davide and De Laet, Tinne and De Raedt, Luc},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2014},
  pages     = {504-507},
  doi       = {10.1007/978-3-662-44845-8_45},
  url       = {https://mlanthology.org/ecmlpkdd/2014/nitti2014ecmlpkdd-distributional/}
}