Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 22, Issue 12, Pages 2011-2021Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2011.2168568
Keywords
Bayesian inference; classification; Gaussian processes; multitask learning
Categories
Funding
- Engineering and Physical Sciences Research Council [EP/F009461/]
- EPSRC [EP/F009461/1, EP/F009461/2] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/F009461/2, EP/F009461/1] Funding Source: researchfish
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We present a novel approach to multitask learning in classification problems based on Gaussian process (GP) classification. The method extends previous work on multitask GP regression, constraining the overall covariance (across tasks and data points) to factorize as a Kronecker product. Fully Bayesian inference is possible but time consuming using sampling techniques. We propose approximations based on the popular variational Bayes and expectation propagation frameworks, showing that they both achieve excellent accuracy when compared to Gibbs sampling, in a fraction of time. We present results on a toy dataset and two real datasets, showing improved performance against the baseline results obtained by learning each task independently. We also compare with a recently proposed state-of-the-art approach based on support vector machines, obtaining comparable or better results.
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