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ILP
2005
Springer

Classifying Relational Data with Neural Networks

9 years 11 months ago
Classifying Relational Data with Neural Networks
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 clai...
Werner Uwents, Hendrik Blockeel
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ILP
Authors Werner Uwents, Hendrik Blockeel
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