Skip navigation
Skip navigation

Fast learning from distributed datasets without entity matching

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


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
Source: IJCAI International Joint Conference on Artificial Intelligence
Access Rights: Open Access


File Description SizeFormat Image
01_Patrini_Fast_learning_from_distributed_2016.pdf2.88 MBAdobe PDFThumbnail

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator