Learning Aggregate Functions with Neural Networks Using a Cascade-Correlation Approach
نویسندگان
چکیده
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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تاریخ انتشار 2008