2006
NeurIPS
NeurIPS 2006
Relational Learning with Gaussian Processes
Abstract
Correlation between instances is often modelled via a kernel function using in- put attributes of the instances. Relational knowledge can further reveal additional pairwise correlations between variables of interest. In this paper, we develop a class of models which incorporates both reciprocal relational information and in- put attributes using Gaussian process techniques. This approach provides a novel non-parametric Bayesian framework with a data-dependent covariance function for supervised learning tasks. We also apply this framework to semi-supervised learning. Experimental results on several real world data sets verify the usefulness of this algorithm.
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- NeurIPS 2006
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Trend Setter
- Nonparametric Bayesian
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Hot Topic Early Bird
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Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Semi-Supervised Learning
Machine Learning > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Core Methods > Kernel Methods
Machine Learning > Bayesian & Probabilistic > Gaussian Processes
Artificial Intelligence > Learning Paradigms > Semi-Supervised Learning
Machine Learning > Bayesian & Probabilistic > Nonparametric Bayesian