Radial basis function network for multi-task learning

Xuejun Liao, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations


We extend radial basis function (RBF) networks to the scenario in which multiple correlated tasks are learned simultaneously, and present the corresponding learning algorithms. We develop the algorithms for learning the network structure, in either a supervised or unsupervised manner. Training data may also be actively selected to improve the network's generalization to test data. Experimental results based on real data demonstrate the advantage of the proposed algorithms and support our conclusions.
Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems
Number of pages8
StatePublished - Dec 1 2005
Externally publishedYes

Bibliographical note

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