caa a22 4500 605478317 CHVBK 20210128100405.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s10994-015-5483-2 doi (NATIONALLICENCE)springer-10.1007/s10994-015-5483-2 Learning relational dependency networks in hybrid domains [Elektronische Daten] [Irma Ravkic, Jan Ramon, Jesse Davis] Statistical relational learning (SRL) is concerned with developing formalisms for representing and learning from data that exhibit both uncertainty and complex, relational structure. Most of the work in SRL has focused on modeling and learning from data that only contain discrete variables. As many important problems are characterized by the presence of both continuous and discrete variables, there has been a growing interest in developing hybrid SRL formalisms. Most of these formalisms focus on reasoning and representational issues and, in some cases, parameter learning. What has received little attention is learning the structure of a hybrid SRL model from data. In this paper, we fill that gap and make the following contributions. First, we propose hybrid relational dependency networks (HRDNs), an extension to relational dependency networks that are able to model continuous variables. Second, we propose an algorithm for learning both the structure and parameters of an HRDN from data. Third, we provide an empirical evaluation that demonstrates that explicitly modeling continuous variables results in more accurate learned models than discretizing them prior to learning. The Author(s), 2015 Statistical relational learning nationallicence Hybrid domains nationallicence Structure learning nationallicence Ravkic Irma Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001, Heverlee, Belgium aut Ramon Jan Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001, Heverlee, Belgium aut Davis Jesse Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001, Heverlee, Belgium aut Machine Learning Springer US; http://www.springer-ny.com 100/2-3(2015-09-01), 217-254 0885-6125 100:2-3<217 2015 100 10994 https://doi.org/10.1007/s10994-015-5483-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-015-5483-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ravkic Irma Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001, Heverlee, Belgium aut NATIONALLICENCE 700 1- Ramon Jan Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001, Heverlee, Belgium aut NATIONALLICENCE 700 1- Davis Jesse Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001, Heverlee, Belgium aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 100/2-3(2015-09-01), 217-254 0885-6125 100:2-3<217 2015 100 10994