Abstract
In various application domains, data can be represented as bags of vectors. Learning functions over such bags is a challenging problem. In this paper, a neural network approach, based on cascade-correlation networks, is proposed to handle this kind of data. By defining special aggregation units that are integrated in the network, a general framework to learn functions over bags is obtained. Results on both artificially created and real-world data sets are reported.
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Uwents, W., Blockeel, H. (2008). Learning Aggregate Functions with Neural Networks Using a Cascade-Correlation Approach. In: Železný, F., Lavrač, N. (eds) Inductive Logic Programming. ILP 2008. Lecture Notes in Computer Science(), vol 5194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85928-4_24
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DOI: https://doi.org/10.1007/978-3-540-85928-4_24
Publisher Name: Springer, Berlin, Heidelberg
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