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Fast learning from distributed datasets without entity matching

Patrini, Giorgio; Nock, Richard; Hardy, Stephen; Caetano, Tiberio

Description

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]

CollectionsANU Research Publications
Date published: 2016
Type: Conference paper
URI: http://hdl.handle.net/1885/154182
Source: IJCAI International Joint Conference on Artificial Intelligence
Access Rights: Open Access

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