Title: Distributional clauses particle filter
Authors: Nitti, Davide
De Laet, Tinne
De Raedt, Luc
Issue Date: 19-Sep-2014
Host Document: European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part III pages:504-507
Conference: Machine Learning and Knowledge Discovery in Databases location:Nancy, France date:September 15-19, 2014
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.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
Tutorial services, Faculty of Engineering

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