Fast learning from distributed datasets without entity matching
Consider the following scenario: two datasets/peers contain the same real-world entities described using partially shared features, e.g. banking and insurance company records of the same customer base. Our goal is to learn a classifier in the cross product space of the two domains, in the hard case in which no shared ID is available -e.g. due to anonymization. Traditionally, the problem is approached by first addressing entity matching and subsequently learning the classifier in a standard...[Show more]
|Collections||ANU Research Publications|
|Source:||IJCAI International Joint Conference on Artificial Intelligence|
|Access Rights:||Open Access|
|01_Patrini_Fast_learning_from_distributed_2016.pdf||2.88 MB||Adobe PDF|
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