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Lookup NU author(s): Yulong Gu, Professor Paolo MissierORCiD
This is the final published version of a conference proceedings (inc. abstract) that has been published in its final definitive form by CEUR-WS, 2017.
For re-use rights please refer to the publisher's terms and conditions.
© by the paper's authors. We consider the problem of incrementally learning models from relational data. Most existing learning methods for statistical relational models use batch learning, which becomes computationally expensive and eventually infeasible for large datasets. The majority of the previous work in relational incremental learning assumes the model's structure is given and only the model's parameters needed to be learned. In this paper, we propose algorithms that can incrementally learn the model's parameters and structure simultaneously. These algorithms are based on the successful formalisation of the relational functional gradient boosting system (RFGB), and extend the classical propositional ensemble methods to relational learning for handling evolving data streams.
Author(s): Gu Y, Missier P
Editor(s): Nicolas Lachiche and Christel Vrain
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 27th International Conference on Inductive Logic Programming (ILP 2017)
Year of Conference: 2017
Pages: 22-26
Online publication date: 29/03/2018
Acceptance date: 02/04/2016
Date deposited: 12/06/2018
ISSN: 1613-0073
Publisher: CEUR-WS
URL: http://ceur-ws.org/Vol-2085/guLBP-ILP2017.pdf
Series Title: CEUR Workshop Proceedings
