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Adaptive incremental learning for statistical relational models using gradient-based boosting

Lookup NU author(s): Yulong Gu, Dr Paolo Missier

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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.

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Abstract

© 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.


Publication metadata

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


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