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- W1542469860 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." @default.
- W1542469860 created "2016-06-24" @default.
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- W1542469860 date "2008-01-01" @default.
- W1542469860 modified "2023-09-27" @default.
- W1542469860 title "Complex aggregates in relational learning" @default.
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- W1542469860 doi "https://doi.org/10.3233/aic-2008-0422" @default.
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