Title: Complex aggregates in relational learning
Authors: Vens, Celine # ×
Issue Date: 2008
Publisher: IOS
Series Title: AI Communications vol:21 issue:2-3 pages:219-220
Abstract: In relational learning, one learns patterns from relational databases, which usually contain multiple tables that are interconnected via relations. Thus, an example for which a prediction is to be given may be related to a set of objects that are possibly relevant for that prediction.
Relational classifiers differ with respect to how they handle these sets: some use
properties of the set as a whole (using aggregation), some refer to
properties of specific individuals, however, most classifiers do not combine both. This imposes an undesirable bias on these learners. This dissertation describes a learning approach that
avoids this bias, using complex aggregates, i.e., aggregates that impose selection conditions on the set to aggregate on.
ISSN: 0921-7126
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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