Multi-Relational Learning with Gaussian Processes
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1309-1313.
Abstract
Due to their flexible nonparametric nature, Gaussian process models are very effective at solving hard machine learning problems. While existing Gaussian process models focus on modeling one single relation, we present a generalized GP model, named multi-relational Gaussian process model, that is able to deal with an arbitrary number of relations in a domain of interest. The proposed model is analyzed in the context of bipartite, directed, and undirected univariate relations. Experimental results on real-world datasets show that exploiting the correlations among different entity types and relations can indeed improve prediction performance.Zhao Xu, Kristian Kersting, Volker Tresp
@inproceedings{ijcai2009p220,
title = {Multi-Relational Learning with Gaussian Processes},
author = {Xu, Zhao and Kersting, Kristian and Tresp, Volker},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {1309--1313},
year = {2009},
url = {https://ijcai.org/proceedings/2009/220},
}
TY - CPAPER TI - Multi-Relational Learning with Gaussian Processes AU - Xu, Zhao AU - Kersting, Kristian AU - Tresp, Volker T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 1309 EP - 1313 UR - https://ijcai.org/proceedings/2009/220 ER -Copied
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