Title: Classifying relational data with neural networks
Authors: Uwents, Werner ×
Blockeel, Hendrik #
Issue Date: 2005
Publisher: Springer
Host Document: Lecture notes in computer science vol:3625 pages:384-396
Conference: Inductive Logic Programming edition:15 location:Bonn, Germany date:August 10-13, 2005
Abstract: We introduce a novel method for relational learning with neural networks. The contributions of this paper are threefold. First, we introduce the concept of relational neural networks: feedforward networks with some recurrent components, the structure of which is determined by the relational database schema. For classifying a single tuple, they take as inputs the attribute values of not only the tuple itself, but also of sets of related tuples. We discuss several possible architectures for such networks. Second, we relate the expressiveness of these networks to the 'aggregation vs. selection' dichotomy in current relational learners, and argue that relational neural networks can learn non-trivial combinations of aggregation and selection, a task beyond the capabilities of most current relational learners. Third, we present and motivate different possible training strategies for such networks. We present experimental results on synthetic and benchmark data sets that support our claims and yield insight in the behaviour of the proposed training strategies.
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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